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PG&E’s Emerging Technologies Program ET13PGE1021

Measurement and Verification for the Zero Net

Energy Stevens Library

ET Project Number: ET13PGE1021

‘

Project Manager: Peter Turnbull and Mananya Chansanchai Pacific Gas and Electric Company Prepared By: Jon Roberts, Shane Mason, Sarah Buddinger, Jim Maclay The Cadmus Group, Inc. 8105 Irvine Center Drive, Suite 150. Irvine, CA 92618

Issued: December 19, 2014


Copyright, 2014, Pacific Gas and Electric Company. All rights reserved.

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ACKNOWLEDGEMENTS

Pacific Gas and Electric Company’s Emerging Technologies Program is responsible for this project. It was developed as part of Pacific Gas and Electric Company’s Emerging Technology – Technology Assessments Program under internal project number ET13PGE1021. The Cadmus Group, Inc. conducted this technology evaluation for Pacific Gas and Electric Company with overall guidance and management from Mananya Chansanchai, Mangesh Basarkar and Peter Turnbull. Anna LaRue and Dr. Carrie Brown from Resource Refocus LLC and Loralyn Perry from Energy Matters provided ongoing technical support, guidance, and review. Sandy Dubinsky and Jeff Barton from Sacred Heart Schools provided were the primary client contacts. The architect was Pauline Souza, AIA, LEED Fellow from WRNS Studio. The MEP Engineer was Interface Engineering. The controls contractor was Environmental Systems Inc. Mark Groark, the Controls Project Manager, programmed trend logs and supported M&V efforts. The civil engineer was Sherwood Design Engineers, Inc. The structural engineer was Hohbach-Lewin, Inc. The landscape designer was Bellinger Foster Steinmetz, The acoustics consultant was Charles M Salter Associates, Inc. The general contractor was Herrero Contractors. We gratefully thank and acknowledge all for their effort and support to make this project successful. For more information on this project, contact Peter Turnbull at [email protected].

LEGAL NOTICE

This report was prepared for Pacific Gas and Electric Company for use by its employees and agents. Neither Pacific Gas and Electric Company nor any of its employees and agents:

(1) makes any written or oral warranty, expressed or implied, including, but not limited to those concerning merchantability or fitness for a particular purpose;

(2) assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, process, method, or policy contained herein; or

(3) represents that its use would not infringe any privately owned rights, including, but not limited to, patents, trademarks, or copyrights.

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ABBREVIATIONS AND ACRONYMS

BMS Building Management System – the controls that manage the various building equipment and provide trend logging

CEC California Energy Commission

CPUC California Public Utilities Commission

CT Current Transducer—Sensor used to measure electrical current

E-Mon D-Mon

Brand name of the electrical power submetering equipment installed in the library

EUI Energy Use Intensity – The amount of annual energy a building uses on a square foot basis, with units

of kBTU/ft2

FC Fan Coil – HVAC system

FDD Fault Detection and Diagnostics – Software tools that take the BMS data and provide automated analysis and fault detection diagnostics and provide actionable data to the building owner/operators

IEPR Integrated Energy Policy Report

IOU California Investor Owned Utilities

M&V Monitoring and Verification – The process of monitoring actual building energy performance and system performance, and verifying that it meets projected building performance goals. The specific monitoring and verification approach is defined in a monitoring and verification plan.

RTU Roof Top Unit

SHS Sacred Heart Schools

ZNE Zero Net Energy – A building that generates as much energy from on-site clean and renewable energy as it uses over the course of the year

ZNE Roadmap

Pacific Gas & Electric Company, “Road to ZNE: Mapping Pathways to ZNE Buildings in California.” December 2012. http://www.energydataweb.com/cpucFiles/pdaDocs/897/Road%20to%20ZNE%20FINAL%20Report.pdf

http://www.energydataweb.com/cpucFiles/pdaDocs/897/Road%20to%20ZNE%20FINAL%20Report.pdf

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FIGURES

Figure 1: Monthly energy balance and YTD cumulative net energy ...... 2

Figure 2: 2013 Annual energy end use distribution (kwh/year, and

% of total building electricity use) .................................. 3

Figure 3: Stevens Library ............................................................ 11

Figure 4: Steven’s Library floor plan ............................................. 15

Figure 5: PV layout (from drawing E401B) ..................................... 16

Figure 6: EC1 Evaporative cooler schedule (from drawing MSK006) .. 17

Figure 7: EC1 evaporative cooling system BMS points and

sequence of operations (from drawing M602B) .............. 18

Figure 8: Split system mechanical schedule ................................... 19

Figure 9: Split system heat pump BMS points, and sequence of

operations ................................................................. 20

Figure 10: Exhaust mechanical schedule ....................................... 20

Figure 11: Exhaust fan controls.................................................... 21

Figure 12: Lighting layout (from Drawing E201B) ........................... 22

Figure 13: DHW specifications (from Drawing 501B) ....................... 23

Figure 14: Rainwater line diagram ................................................ 23

Figure 15: Graywater irrigation 1-line diagram ............................... 24

Figure 16: Graywater potable 1-line diagram ................................. 25

Figure 17: Monthly energy balance and year-to-date cumulative

net energy ................................................................ 33

Figure 18: 2013 vs 2014 monthly library electricity use .................. 34

Figure 19: Net energy balance ..................................................... 34

Figure 20: 2013 annual energy end use distribution ....................... 36

Figure 21: Monthly energy end use distribution .............................. 37

Figure 22: Stevens Library benchmarked against ZNE best practice

guidelines ................................................................. 38

Figure 23: EnergyIQ benchmark data for all California central coast

schools ..................................................................... 39


elementary and middle schools .................................... 40


office and school buildings ........................................... 41

Figure 26: Heating degree data during the M&V period ................... 44

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Figure 27: Cooling degree data during the M&V period .................... 45

Figure 28: Average maximum monthly temperature during the

M&V period................................................................ 46

Figure 29: Average mean monthly temperature during the M&V

period ....................................................................... 47

Figure 30: Average minimum monthly temperature during the M&V

period ....................................................................... 47

Figure 31: Average relative humidity during the M&V period ............ 48

Figure 32: Average wind speed during the M&V period .................... 48

Figure 33: Average cloud cover during the M&V period ................... 49

Figure 34: Original PV performance projections for 30o tilt ............... 50

Figure 35: Revised PV performance projection for panels flat on

roof .......................................................................... 50

Figure 36: PV output (kW) during the initial system performance

analysis (November – December 2012) ......................... 51

Figure 37: October 2013 PV data showing gaps in logged data

(representative of 2/13 – 12/13 PV data) ...................... 52

Figure 38: Incident solar radiation versus PV output for November

2012 through January 2013 ......................................... 53

Figure 39: Comparison of PV output design projections to 2013 and

2014 (partial year) generation data .............................. 53

Figure 40: 2013 estimated and 2014 measured PV generation ......... 54

Figure 41: Average monthly cloud cover for March-August .............. 55

Figure 42: 2013 and 2014 measured HVAC electricity consumption .. 55

Figure 43: EC1 operating characteristics ....................................... 56

Figure 44: EC1 temperature details for November 2012 .................. 57

Figure 45: EC1 space CO2 levels for November 2012 ...................... 57

Figure 46: Average hourly HVAC kW for the initial M&V period

(October through December 2012) ............................... 58

Figure 47: Average hourly HVAC kW for a typical heating season

week (11/26 – 12/2 2012) .......................................... 58

Figure 48: Average hourly HVAC kW for a typical day (Friday

11/30/12) ................................................................. 59

Figure 49: Daily HVAC kWh for the initial M&V period and heating

degree days (HDD) for (October through December

2012) ....................................................................... 60

Figure 50: Daily HVAC kWh vs. heating degree days ....................... 60

Figure 51: EC1 HVAC unit function status (October 2013) ............... 61

Figure 52: EC1 HVAC unit CO2 and Temperatures (October 2013) .... 62

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Figure 53: Daily temperature Ranges (October 2013 ...................... 63

Figure 54: Room 201 tutoring room temperature (November 2012) . 64

Figure 55: Room 204 tech room temperature (November 2012) ...... 64

Figure 56: Room 206 electrical room temperature (November

2012) ....................................................................... 64

Figure 57: Room 207 temperature (November 2012) ...................... 65

Figure 58: Room 211 office temperature (November 2012) ............. 65

Figure 59: Room 201 tutoring room temperatures (10/13 – 11/14) .. 66

Figure 60: Room 204 tech room temperatures (10/13 – 11/14) ....... 66

Figure 61: Room 206 electrical room temperatures (10/13 –

11/14) ...................................................................... 67

Figure 62: Room 207 meeting room temperatures (10/13 – 11/14) .. 67

Figure 63: Room 211 office temperatures (10/13 – 11/14) .............. 67

Figure 64: Monthly lighting energy use for the entire M&V period ..... 69

Figure 65: Measured lighting power density (LPD) for November

2012 ........................................................................ 70

Figure 66: Lighting power density for a typical day (Monday,

11/12) ...................................................................... 71

Figure 67: Monthly lighting energy use for the entire M&V period ..... 72

Figure 68: DHW measured electricity use for October and

November 2012 ......................................................... 72

Figure 69: Monthly rainwater and graywater system electricity use .. 73

Figure 70: 2013 and 2014 comparative monthly rainwater and

graywater system electricity use .................................. 74

Figure 71: Rainwater/graywater system average daily electricity

consumption (kWh, and % of rainwater/graywater

total) ........................................................................ 75

Figure 72: Monthly plug load and ceiling fan electricity use .............. 75

Figure 73: Design energy model end use breakout ......................... 81

Figure 74: As-built energy model end use breakout ........................ 82

Figure 75: Initial calibrated energy model end use breakout for

2013 ........................................................................ 86

Figure 76: Comparison of measured energy vs. initial calibrated

energy model projections ............................................ 88

Figure 77:plut calibrated energy model end use breakout ................ 90

Figure 78: Comparison of 2013 and 2014 measured building

energy vs. final calibrated energy model ....................... 91

Figure 79: E-Mon D-Mon panel labeling ......................................... 95

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Figure 80: Updated calibration factor for the BMS’ lighting panel

(panel BH-1) submeter ............................................... 97

Figure 81: Comparison of actual (Dent), mis-calibrated (BMS) and

corrected BMS lighting power measurements (11/1/12

through 12/4/12) ....................................................... 98

Figure 82: Comparison of actual (Dent), mis-calibrated (BMS) and

corrected BMS lighting power measurements (11/26/12

through 11/30/12) ..................................................... 98

Figure 83: Comparison of measured total building power vs. BMS

reported building power .............................................. 99

Figure 84: Comparison of measured total building power vs.

corrected BMS power (corrected lighting) .................... 100

Figure 85: Example of SkySpark FDD software interface ............... 105

Figure 86: Building B electrical 1-line drawing .............................. 111

Figure 87: Mechanical equipment electrical connection summary ... 112

Figure 88: Distribution switchboard DP-BH schedule ..................... 112

Figure 89: Panel B-H1 schedule ................................................. 113

Figure 90: Panel LCP B-H1 schedule ........................................... 113

Figure 91: Panel B-L1 schedule (as-built) .................................... 114

Figure 92: Panel B-L2 schedule (as-built) .................................... 115

Figure 93: BMS 1-line diagram ................................................... 116

TABLES

Table 1: BMS trend data points .................................................... 28

Table 2: Data logger and spot measurement installation summary ... 30

Table 3: Data logger configuration details ..................................... 31

Table 4: Monthly building electricity consumption and generation

Data ......................................................................... 35

Table 5: annual and M&V period summaries of building electricity

consumption and generation Data ................................ 36

Table 6: Library equipment and plug loads count ........................... 43

Table 7: Heating degree data during the M&V period ...................... 45

Table 8: Cooling degree data during the M&V period ....................... 46

Table 9: Comparison of 2013 actual energy use and design energy

model ....................................................................... 81

Table 10: Comparison of 2013 actual energy use to design and as-

built energy models .................................................... 83

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Table 11: Comparison of actual Atherton to CACZ04 temperatures

for November 2012 .................................................... 84

Table 12: Initial calibrated energy model projections compared to

November 2012 M&V data ........................................... 85

Table 13: Comparison of 2013 actual energy use to design, as-built

and initial calibrated energy models .............................. 86

Table 14: Comparison of 2013 actual energy use to design, as-built

and initial calibrated energy models .............................. 92

Table 15: Comparison of measured vs. updated energy model total

building energy consumption (kWh) ............................. 92

Table 16: E-Mon D-Mon system CT placement ............................... 94

Table 17: Library equipment and plug loads count ........................ 121

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CONTENTS

EXECUTIVE SUMMARY 1

INTRODUCTION 11

BACKGROUND 12

EMERGING TECHNOLOGY 14

ASSESSMENT OBJECTIVES 26

TECHNOLOGY EVALUATION 26

TECHNICAL APPROACH 26

RESULTS 32

EVALUATIONS 101

RECOMMENDATIONS AND LESSONS LEARNED 101

APPENDIX A: ADDITIONAL ELECTRICAL AND MECHANICAL SYSTEM DETAILS 110

APPENDIX B: OPERATIONAL SURVEY 117

APPENDIX C: SCHOOL SCHEDULE 122

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EXECUTIVE SUMMARY

PROJECT GOAL

The objective of this project is to evaluate how the Sacred Heart Schools Stevens Library

performed with respect to its zero net energy design goal, and to identify issues, challenges,

problems, and lessons learned to inform and guide the design of future ZNE buildings.

PROJECT DESCRIPTION

Sacred Heart Schools completed construction of its Stevens Library for lower and middle

school students in August 2012. The library is a 6,300 ft2 all-electric building. It has been

designed and constructed to achieve Zero Net Energy (ZNE) performance (based on total

site energy), meet the Living Building Challenge and attain LEED Platinum certification. The

project includes many energy efficiency and high performance features to achieve these

goals, including mixed mode natural ventilation design, high performance glazing, efficient

lighting and controls, high thermal resistance cool roof, variable speed split system

conditioning, direct/ indirect evaporative cooling, ceiling fans, and significant energy sub-

metering. Note that the library was originally designed with a natural gas heated,

direct/indirect evaporatively cooled roof top unit (RTU). This unit was replaced with an

electric heat pump RTU (also with direct/indirect evaporative cooling) due in part to

concerns about meeting the ZNE goals. The original ZNE projections were based on an

optimally tilted PV array, but the final design had the PV system installed horizontally on the

roof which reduced PV output. To compensate for the reduced PV output, the more efficient

(from a site-energy perspective) electric heat pump unit was specified.

The project has received technical assistance from Pacific Gas and Electric Company (PG&E)

to evaluate how well it met its ZNE design goals. PG&E has contracted the Cadmus Group to

provide Measurement and Verification (M&V) services. Initial M&V was provided for October

through December 2012 through the PG&E ZNE Pilot Program. Additional M&V was provided

through the PG&E Emerging Technologies Program to extend the M&V period through

September 2014.

PROJECT FINDINGS/RESULTS

The building is exceeding its ZNE performance goals by a significant margin. For 2013 the

PV system generated 54% more electricity than the building consumed. The trend is similar

for 2014. For the entire monitoring period (12/12 – 9/14), the PV system has generated

73% more electricity than the building has consumed. Figure 1 plots the monthly energy

balance (building electricity use and PV generation) and year-to-date (YTD) cumulative net

energy1. There is significant excess solar energy generation during the summer. This is due

to a combination of reduced summer occupancy and increased solar generation. The

building is very efficient and has an energy use intensity (EUI) of 18.0 ± 0.6 kBTU/

1 Net energy = PV generation – building use; positive means net electricity generation,

negative means net electricity use.

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ft2/year2. This is significantly lower than the New Building Institute (NBI) “Getting to Zero”

Report3’s definition of “ZNE Capable” buildings4.

FIGURE 1: MONTHLY ENERGY BALANCE AND YTD CUMULATIVE NET ENERGY

Figure 2 summarizes the overall energy end use distribution for 2013 (for which a complete

year’s worth of data is available). Space conditioning consumes just over 60% of the

building’s energy and is the biggest load. Of the space conditioning load, fans account for

32%, heating for 48%, cooling for 15%, and pumps and controls for the remaining 5%.

Lighting and non-regulated loads primarily under occupant control (e.g., plug loads, ceiling

fans) both consume equal amounts at ~15% of the total building load each. The

rainwater/graywater system consumes 8% of the building energy use. These loads include

a variety of pumps, ozone generators and related equipment.

2 Average annual EUI based on the nearly 12/12 – 9/14 data. 3 New Buildings Institute. “Getting to Zero 2012 Status Update: A First Look at the Costs and

Features of Zero Energy Commercial Buildings.” March 2012.

http://newbuildings.org/sites/default/files/GettingtoZeroReport_0.pdf 4 NBI defines ZNE Capable buildings as buildings with EUIs ≤ 35 kBTU/ft2/year. They do not

differentiate by use type.

-5,000

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10,000

15,000

20,000

25,000

30,000

-2,000

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2,000

4,000

6,000

8,000

10,000

12,000

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Cu

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2013 YTD Cumulative Net Energy


Building Electricity Consumption

PV Generation

Estimated PV Generation

http://newbuildings.org/sites/default/files/GettingtoZeroReport_0.pdf

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FIGURE 2: 2013 ANNUAL ENERGY END USE DISTRIBUTION (KWH/YEAR, AND % OF TOTAL BUILDING ELECTRICITY USE)

PROJECT RECOMMENDATIONS This M&V project has resulted in a number of recommendations and lessons learned that

may be useful to a variety of stakeholders. The following discussion is organized by topic

with by specific recommendations for key stakeholder groups.

SUB-METERING

Metering and submetering are critical to ZNE buildings. Without proper, accurate and

sufficient metering it is impossible to track building performance and manage the building to

achieve and maintain ZNE status. This is particularly critical at this facility as the building is

part of a larger master-metered campus and does not have its own utility meter.

Stevens Library is equipped with six electric submeters to monitor and manage electricity

use. There were several metering related issues that this M&V project identified which would

likely have otherwise gone unnoticed. First, two of the current transducers (CTs) in

submetering panel did not match the submeters’ requirements and resulted in incorrect

readings. The CT measuring the lighting loads was a 200 Amp CT, but the submeter was

configured to read a 100 Amp CT. This mismatch resulted in inaccurate readings. The

lighting loads are being recorded by the BMS at 4.86 times lower than actual loads. This

error is compounded because the BMS adds all of the submetered loads to get total building

energy use. The incorrect lighting panel reading resulted in total building energy being

reported as ~20% lower than actual energy use. It is possible that the error could have

gone the other way and reported higher than actual energy use. This has the potential to

jeopardize a building’s ability to demonstrate that is has achieved ZNE. This is problematic

to all parties involved, particularly if an important sustainability program performance rating

is at stake (i.e., loss of LEED energy and atmosphere credit 1 points for energy efficiency;

inability to meet the living building challenge). The M&V team flagged the CT/submeter

Space Conditioning, 21,367 , 61%

Lighting, 5,211 , 15%

Water Heating, 80 ,

0.2%

Rainwater/ Graywater

System, 2,750 , 8%

Ceiling Fans and Plug

Loads (Panel B-L2), 5,715 ,

16%

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mismatch issue on the lighting panel during the initial M&V period and had thought that the

issue had been resolved. During the data review and QC for the final report, unusually low

lighting power densities were observed and investigated, and it was determined that the

lighting panel CT had not been changed. This was flagged and Sacred Heart Schools has

replaced the sensor.

Metering problems have been a common issue for several recent ZNE buildings. Metering is

a specialty field that, depending on the metering equipment involved, can require special

expertise to install and calibrate. It is recommended that all metering and submetering

equipment in ZNE and high performing buildings receive appropriate commissioning and

verification to ensure it is providing accurate data.

RECOMMENDATIONS TO SACRED HEART SCHOOLS

Note that historical BMS lighting and total building power data prior to the

12/2014 CT change-out is inaccurate. The calibration multiplier provided in

this report can be used to update historical data if needed. Note that all data

presented in this report has been updated.

One submeter is currently unused. It would be valuable to operations to

connect this and pick up power or current data for EC1 and the duct heaters.

The submeter readings for the other new buildings should be spot checked

with a hand-help multimeter to confirm their accuracy.

RECOMMENDATIONS TO DESIGN TEAMS

Metering and submetering is often left out of commissioning scopes and

metering problems often go unnoticed. The design team should ensure that

the commissioning scope includes commissioning for submeters. This is

particularly critical for ZNE buildings and other high performing buildings

where having accurate data is vital to achieving and maintaining performance

goals. It is not inconceivable to envision a situation where a building would

“fail” to achieve ZNE or properly document performance due to a simple

metering issue and miss out on LEED points or miss out on achieving a rating

(i.e., the Living Building Challenge).

The design team on this project did a great job designing the electrical

circuits to facilitate easy submetering. However, many building electrical

layouts present significant challenges and preclude easy and inexpensive

submetering. Design teams should make sure to include submetering

requirements in the project and ensure that appropriate design team

personnel are aware of these requirements.

RECOMMENDATIONS TO UTILITIES AND POLICY MAKERS

Metering and submetering issues are a common theme observed on multiple

projects. There are opportunities to encourage projects to improve the

submetering and metering process and make this data more useable and

useful for building owners to achieve and maintain ZNE or similar energy

performance goals.

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BMS TREND DATA

This project had excellent BMS trend data to work with. This was invaluable to the M&V

efforts and will be extremely useful for the school to help manage and maintain its ZNE

status. Part of the success was due to the fact that the M&V consultant was able to

coordinate with the controls contractor before the BMS programming was finished, and it

was easy to set up the desired trend logs. Unfortunately, this is not always the case on

projects. More often than not the rich BMS data vital to managing ZNE and deep energy

efficiency is very difficult for building operators, M&V personnel, and others to access, and

therefore it is not used to the extent it could be.

Another issue encountered in the BMS trend data is that the PV power and energy points

were configured to log data on a change in value, rather than a fixed time increment. This

resulted in a massive amount of data (tens of thousands of records per year) that is very

difficult to utilize. Each time increment is different, so it is difficult to overlay this data with

other data (i.e., building performance data). It is possible that the very heavy

communication load placed on the inverter’s communication module to report data on such

a frequent basis may have contributed to the problems it experienced.


Reviewing the BMS trend logs on a monthly basis through the M&V program

has been very useful in identifying issues and spotting problems early and is

critical to maintaining long term ZNE status.

Explore opportunities to automate the routine monthly BMS trend log

downloads and include key performance indicators on the building dashboard.

Examples include plotting monthly energy use against calibrated modeled

energy use. Any significant deviation from monthly expectations could help to

identify and respond to significant issues early.


It would be valuable to develop a coordinated M&V approach that outlines key

BMS data to trend, time increment for trending the data, defining which

circuits need to be submetered, and give thought to how various stakeholders

charged with meeting ZNE performance goals will be able to access the data

in a quick and easy way. Leaving these as ad-hoc decisions that the controls

contractor has to make on the fly is not optimal for leveraging the usefulness

of the BMS data for meeting ZNE goals.

Downloading and processing BMS trend data remains a complex and time

consuming job for building O&M personnel. The design team and controls

contractor should jointly consider opportunities in specifications and control

system selection that would help facilitate ready use of appropriate BMS data

vital to managing and attaining ZNE and related performance goals.


Design teams are becoming increasingly accountable for building performance

and will have an increased stake in how well buildings perform. There are

numerous opportunities for the design team to enhance the effectiveness of

monitoring, controls, and related systems through thoughtful design. There

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are significant opportunities to further explore and promote these

opportunities from the utility and policy side.

AUTOMATED FAULT DETECTION AND DIAGNOSTICS

The BMS system produces a large amount of very useful data that are being stored in trend

logs. Unfortunately, this data is not always easily accessible to typical building owners and

operators. The data must be manually downloaded to a spreadsheet and processed, which is

a time consuming process. Typically, this data is rarely reviewed and therefore provides

little actionable information to inform building owners/operators on an ongoing basis.

Making better use of this data will be crucial for ensuring ongoing ZNE performance by the

Library and other ZNE buildings.

An emerging set of complementary automated “Fault Detection and Diagnostic” (FDD)

software tools and related building dashboard tools are coming onto the market which will

facilitate use of this detailed BMS data and automate much of the labor-intensive review and

processing. While the building automation system is capable of controlling equipment, data

display, alarming and trending, it is not capable of detailed fault detection and

troubleshooting. Fault detection and diagnostics software is capable of conducting custom

detailed analysis on the data handled by the building automation system and serving it in a

graphical method that is intuitive to the user. The appropriate fault detection system, much

like the automation system, is flexible enough to be modified and updated to accommodate

future changes to systems and sequences of operation. This software package gives the

user the capability to run analytics across the entire range of control points within the

automation system, generate and distribute alarms, display data graphically and make

corrections to setpoints and schedules accordingly.

Ongoing commissioning of the building systems is the primary intent of the automated fault

detection system. While commissioning and re-commissioning of systems is effective for

instantaneous verification of correct system operation, fault detection systems continue to

watch building systems long after start up and initial testing is complete. The combination of

ongoing monitoring and custom analytics provides a platform for continued system

optimization and a real-time view of the buildings energy consumption.


Explore opportunities to expand the Lucid Designs dashboard to include some

of the automated diagnostics and fault detection reporting that will help the

Steven’s library maintain ZNE status and minimize facilities impact for

downloading and processing BMS data. This could include things such as

comparing monthly building EUI to predicted EUI (from calibrated model) and

reporting significant deviations.


Design teams will want to watch this nascent field carefully. There are some

very exciting developments that may be useful to incorporate into high

performing building projects to help ensure challenging performance targets

are met.

7



AFDD is an exciting development in the building industry and has significant

potential to help improve long term building performance. Emerging

technology studies and similar efforts to document performance impacts and

best practices with AFDD systems would be very valuable.

ENERGY MODELING

Building energy modeling is used and applied in different ways depending on what one is

trying to accomplish. Each use has a unique set of practitioners, goals, and established

approaches to building energy modeling. Traditionally there has been limited cross-over

between each of these different building energy modeling domains, their practitioners, their

targeted building lifecycle phase. Each domain requires a niche expertise, and involves

different stakeholders, customers, team-members and building phases. The rise of ZNE

buildings creates very interesting cross-over opportunities between the different energy

modeling domains. The building energy model now becomes a critical tool for (1) optimizing

building energy performance in the early design phase, (2) documenting compliance, (3)

accurately projecting actual building performance during operations to size the onsite

renewable system and meet ZNE performance requirements, (4) verifying ZNE performance

and “correcting” for atypical weather, occupancy, and other operational issues as is done for

guaranteed energy savings projects, and (5) facilitating building operations personnel to

maintain ZNE operations. There is need for increased education about the different ways

energy modeling can and needs to be applied to ZNE buildings.


ZNE building projects will require a higher level of modeling accuracy and

applying energy modeling for different purposes. Increased design team use

of building energy modeling is required and team energy modeling expertise

must generally increase as well..

Design teams need to understand that there are different uses for energy

modeling throughout the project life cycle, and effectively use energy

modeling at each phased.

Design teams should be very careful to understand the difference between a

“compliance energy model” and an energy model used to estimate actual

building operational energy for ZNE renewable energy system sizing.

A final “as built” energy model should be developed and used to confirm ZNE

estimates.

Appropriate safety margins should be built into ZNE renewable energy system

sizing to account for weather, occupancy, schedule, space use intensity, and

plug load variance that are likely to occur.

Standard assumptions for plug loads, DHW, and other loads which do not

typically matter as much in compliance modeling (since they are assumed

equal in both the design and base-case and do not typically appreciably

impact compliance energy savings projections) should be very carefully

evaluated. These loads are often significantly different from actual building

loads and poor estimates can jeopardize a building’s ability to achieve ZNE.

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There is a significant need to develop modeling guidelines and best practices

for practitioners to transition from “compliance” modeling to “performance”

modeling. The National Renewable Energy Laboratory’s Building America

Program, for example, developed a set of energy modeling guidelines and

data for residential energy modeling that were very useful to practitioners.

Similar approaches could be taken for commercial building modeling. Existing

databases (i.e., CUESS) could be leverage to help develop guidelines for

water heating energy use and other relevant loads. Water heating energy use

was significantly over-estimated for this project.

PLUG LOADS

Plug loads comprise an increasingly large percentage of the total building energy use as

HVAC and other regulated loads are reduced. It is not uncommon for plug loads to represent

25% - 50% of a ZNE building’s total load. The Library’s plug loads are relatively small

compared to typical buildings, accounting for 16% of the total building energy use in 2013.

The original energy model over-estimated plug load energy by ~50%.

Note that a significant upward trend in plug load energy began in July 2014 and continued

through the end of the M&V period in September 2014. Refer to Figure 72. The reasons for

this increased consumption is unclear, but could include significant new equipment

additions, equipment not being turned off, some type of equipment malfunction, the use of

a portable electric heater(s), or similar issues.. The reasons for this should be investigated

and corrected if needed by Library staff.

The key lessons learned are that plug loads represent a large portion of building energy use

and focusing on opportunities to reduce these loads will be important for future ZNE

buildings. Furthermore, it is important to refine energy modeling efforts to estimate these

as accurately as possible. There may be opportunities for PG&E and other organizations to

support projects to improve the modeling of plug loads. As a starting point, it would be

useful to document how well plug loads are currently being modeled (e.g., a study

comparing LEED building energy modeled data vs. actual plug loads).

ELECTRICAL ROOM TEMPERATURE SETPOINT The electrical room, which contains a number of servers, is maintained between 66 oF and

69.5oF. Typically, servers have higher permissible operating temperature ranges. The

servers’ temperature specifications should be reviewed, and the temperature setpoint

relaxed accordingly to reduce air-conditioning energy use.


Investigate equipment temperature operating limits and increase temperature

setpoints if possible.


Explore opportunities to specify equipment with robust temperature operating

ranges, and make sure this information is communicated to building owners,

9


control contractors who set initial temperature setpoints, commissioning

agents, and other building operations stakeholders.

LIGHTING

The Library uses linear fluorescent lighting with daylighting and occupancy controls to

reduce peak lighting power density (LPD). LED lighting is becoming increasingly cost

effective and can be merged with advanced control strategies, individually controlled

luminaires, and advanced control strategies to minimize lighting energy.


Design teams should specify LED lighting and advanced control strategies that

are well matched to LED lighting technology.


Programs such as the Emerging Technologies Program provide invaluable

information to the design community on what works and what does not,

costs, and other barriers and opportunities related to the installation and

performance of emerging products and technologies. There is an ongoing

need for this information regarding emerging lighting technologies and

practices (i.e., “occupant specific lighting”).

ONSITE WATER RECYCLING AND RAINWATER CAPTURE

Nearly 10% of the library’s energy is spent on the rainwater and graywater systems.


No electricity use was measured on the graywater system’s UV system. The

facility should check to ensure the UV system is operating correctly.


Consider the energy impacts of onsite water systems. Make sure to include

these loads in the relevant ZNE and PV array sizing calculations if they are to

be included in the “ZNE” load. Specify efficient and appropriate equipment

and systems and ensure they are performing as expected.


Water/energy/carbon nexus issues are increasingly becoming a part of

building-level design. This is an area where designers could use guidance on

best practices.

VENTILATION AND AIR QUALITY

The operational survey indicated there is a tendency for the building to feel hot or stuffy

during high occupancy periods or hot weather. Occupants use windows (natural ventilation)

for supplemental ventilation. There are a number of potential reasons for this condition,

10


which are beyond the scope of this M&V effort to fully investigate. The M&V efforts did note

that there is limited compressor use for the main reading room, and it is possible that this

system is not providing adequate humidity control. Room humidity is not one of the trend

logs available on the BMS. It is possible that fine-tuning of the controls could address this

(e.g., CO2 level setpoints, evaporative cooling staging, supply air humidity control

setpoints), or it could be that the building is operated per its design intent, and that some

occupant education on the building design and use of the natural ventilation and ceiling fan

features to provide additional airflow could address the issues.


Investigate humidity levels if building occupants continue to note hot and

stuffy conditions. Fine-tuning of minimum ventilation rates and demand

controlled ventilation controls sequences may be required.

CEILING FANS

The operational survey indicates that the ceiling fans are noisy and are not used often.


Issues such as noise have a demonstrated impact on occupants use of the

ceiling fans and other equipment. Designers should carefully consider noise

and related issues which may impact user acceptance and use of equipment

and strategies.

In summary, Stevens Library is performing very well and meeting its ZNE goals. The most

important recommendations to the facility is to make sure that the incorrectly sized CT’s on

the BMS electricity submeters are replaced with the correct sized CTs, or have the updated

calibration factor programmed into the BMS. We also strongly recommend that Sacred Heart

Schools continue some type of M&V for not just the library, but all of its buildings to ensure

efficient and cost effective operations. For the design team, the most significant

recommendations for future projects would be to continue refining the energy modeling

process. Plug load, rainwater/graywater system, and DHW heating energy projections were

significantly off. This does not significantly impact this building, but these mis-estimates

could significantly impact ZNE attainment for another building type. Also, the design team

did an excellent job designing the electrical system to be well metered, and included a front

end dashboard. At this point however, it will most likely take strong design team leadership

to ensure that the data logging capabilities are translated into useful and actionable data on

the dashboard that will help building managers maintain long-term ZNE performance. From

the utility perspective, there are significant opportunities more effectively incorporate

submetering into buildings and work with controls contractors, building dashboard

developers, and building operators to make this data useful and actionable. Automated fault

detection and diagnostics will play an important role in managing the massive amounts of

data that submeters and BMS systems generate.

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INTRODUCTION This report presents the results of a detailed 24 month M&V of the Sacred Heart Schools

Stevens Library. The Stevens Library is for lower and middle school students, and was

designed to be zero net energy (ZNE), meet the Living Building Challenge, and achieved a

LEED Platinum Rating.

FIGURE 3: STEVENS LIBRARY5

The library consists of a main reading room with library stacks, and six smaller rooms

(librarian’s office, a classroom/meeting room, a technology/media storage room, technology

coordinator’s office, tutoring room, and tutoring office). The library also contains two single

occupant staff bathrooms, a janitor closet, a storage closet, an electrical room, and a

mechanical room for the rainwater and graywater system equipment.

The library is typically open between 7am-4:30pm weekdays during the school year. Peak

occupancy is typically around 30 students, although up to 50 students may be present

during standardized testing. The librarian’s estimate of total daily occupancy is between 75

to 100 students per day. Some minor weekend occupancy occurs for meetings and other

5 Photo used by permission of the Architect Pauline Souza from WRNS Studios.

12


activities. The school year typically runs from August 28 through December 21 (fall

semester) and January 8 through June 7 (spring semester). Refer to Appendix C for the

detailed academic calendar during the M&V period. The library receives minimal student use

during the summer, but is open and used by staff.

The monitoring and verification (M&V) period was from October 2012 through September

2014. The start of the M&V period coincided with building completion and initial occupancy.

Complete and consistent performance data for the first two months of the M&V period

(October and November 2012) are not available due to BMS and trend logs programming,

metering installation, and various startup-related issues. Full energy performance data is

available for December 2012 through September 2014. The library was occupied during this

period per routine school schedules and activities. Analysis includes evaluation of ZNE

performance, energy end use break-outs, comparison of the design building energy model

to actual data, and building energy model recalibration. Issues and lessons learned are

documented.

BACKGROUND The concept of ZNE buildings began to gain popularity in the 2000’s. Although still few in

number, ZNE buildings have rapidly caught both public and policy-maker attention.

Currently, ZNE buildings are being built on a voluntary basis. However, green building

certification programs are generally moving towards performance based building energy

requirements (i.e., ZNE), with a few programs including the Living Building Challenge

requiring ZNE. Moreover, ZNE requirements are rapidly becoming embedded in a wide

range of energy policy goals.

At the federal level, Executive Order 13514 requires all new Federal buildings that enter the

planning process in 2020 and thereafter to be designed to achieve ZNE standards by 2030.

Some federal agencies have established more ambitious ZNE goals. The Department of

Defense has goals to reduce building energy intensity 37.5% by 2020 and to have 20% of

all facility electricity supplied by renewable energy6. The Navy has a goal of producing at

least 50% of shore-based energy requirements from alternative sources, and for 50% of

Navy installations to be ZNE7. The Department of State currently requires new embassy and

consulates to go through a rigorous comprehensive sustainability planning process to

identify strategies, costs, and benefits for making new facilities ZNE, zero net water and

carbon neutral8.

ZNE building goals are deeply embedded in California Energy Policy. The 2008 California

Long Term Energy Efficiency Strategic Plan (Strategic Plan) included “big bold” goals that all

new residential construction in California be zero net energy (ZNE) by 2020, and all new

commercial construction be ZNE by 2030. The California Public Utilities Commission (CPUC)

has been working with stakeholders to help achieve these ZNE goals through various

6 Department of Defense. “Strategic Sustainability Performance Plan, FY 2011.”

http://www.denix.osd.mil/sustainability/upload/DoD-SSPP-FY11-FINAL_Oct11.pdf 7 Department of the Navy. “Energy Program for Energy and Independence”

http://greenfleet.dodlive.mil/files/2010/04/Naval_Energy_Strategic_Roadmap_100710.pdf 8 Department of State. “2011 U.S. Department of State Agency Sustainability Plan: FY 2010

– FY 2020”. http://www.state.gov/m/pri/rls/plans/176092.htm

http://www.denix.osd.mil/sustainability/upload/DoD-SSPP-FY11-FINAL_Oct11.pdf

http://greenfleet.dodlive.mil/files/2010/04/Naval_Energy_Strategic_Roadmap_100710.pdf

http://www.state.gov/m/pri/rls/plans/176092.htm

13


regulatory and voluntary processes. The California Energy Commission (CEC) has adopted

the ZNE goals as part of their long term planning through the 2007 Integrated Energy Policy

Report (IEPR)9. The California Investor Owned Utilities (IOUs) are pursuing the ZNE goals

outlined in the Strategic Plan and the IEPR. This includes supporting ZNE demonstration

projects, providing energy efficiency incentives and conducting studies to assess the ZNE

goals. PG&E’s ZNE Pilot Program was a part of these efforts.

The PG&E ZNE Pilot Program launched in 2010 and was active through 2012. It focused on

achieving maximal energy efficiency and load reduction by leveraging advanced design,

construction and building operations before the addition of on-site renewable energy

generation, such as solar PV. The ZNE Pilot Program promoted California’s long term energy

goals through a portfolio of research, development, and demonstration (RD&D) projects

around ZNE buildings together with complementary education, outreach and information

activities. After 2012, PG&E has continued its ZNE activities through other programs,

including the Emerging Technologies Program, which supported this project.

There are a number of challenges associated with designing ZNE buildings. ZNE is inherently

performance based—a building is ZNE based on its actual energy use over the course of a

year. However, as part of the design process, buildings are often designed to ZNE goals.

Similar to the current process of documenting building energy code compliance (i.e., Title

24 Building Energy Efficiency Standards) and LEED energy performance, where building

energy consumption is estimated through building energy simulation and compared to a

hypothetical code compliant building’s estimated energy use using a common set of

schedules and assumptions, designers must use a set of assumptions to estimate how well

the building will likely be able to achieve ZNE, once it is constructed and occupied. However,

achieving ZNE is dependent upon many factors outside of the design team’s control, such as

occupant behavior, operations and maintenance (O&M) practices, facility use intensity,

scheduling, and setpoints.

ZNE buildings also typically include deep energy conservation measures. Some designers

rely on state-of-the art systems and controls, whereas others utilize simpler passive and

low-energy design strategies. Regardless of approach, achieving the energy performance

necessary to achieve ZNE is challenging and requires all systems to work as intended.

Occupant behavior and their interaction with the building is a critical aspect to achieving

ultimate ZNE performance in ZNE buildings. As regulated loads (e.g., HVAC, lighting) are

made more efficient through energy standards, appliance standards, and other policy levers,

they are becoming smaller and smaller portions of total building energy use. Unregulated,

occupant driven loads are becoming much more significant and must be addressed to

achieve ZNE. Improved understanding of how occupants drive building energy use, and how

design can help minimize these loads, is increasingly important.

Another challenge associated with creating ZNE buildings is that there are so few actual ZNE

buildings in operation. As part of its ongoing ZNE efforts, PG&E has been performing

detailed performance M&V of new ZNE buildings. This helps fill a critical data gap. For more

information on these and related ZNE issues, refer to “The Road to ZNE: Mapping Pathways

to ZNE Buildings in California10” (ZNE Roadmap) — a project funded and supported by

PG&E’s ZNE Pilot Program. From the final study report:

9 California Energy Commission. http://www.energy.ca.gov/2007_energypolicy/ 10 Pacific Gas & Electric Company, “Road to ZNE: Mapping Pathways to ZNE Buildings in

California.” December 2012.

http://www.energydataweb.com/cpucFiles/pdaDocs/897/Road%20to%20ZNE%20FINAL%20

Report.pdf

http://www.energy.ca.gov/2007_energypolicy/



14


“The essential challenge for achieving the ZNE goals is to learn from the experiences

of the early adopters and apply those lessons learned to motivate, and if needed,

mandate changes. We say this because achieving the ZNE goals will require the type

of rapid changes in current industry practices for design, construction and operation

that cannot be achieved through incentives alone. Relative to many other industries

the construction industry as a whole is not an industry that innovates at a fast pace

on a large scale. Our interviews with stakeholders demonstrate that the majority of

the construction industry will only adopt any ZNE metric as a construction practice

once two things are clear: (1) there is a sustained market demand for that metric of

ZNE; and (2) the resulting buildings are deemed cost-effective and ‘feasible’ by

market actors and building owners/operators.”

This project helps learn from the early adopters by providing well-documented ZNE building

performance data and lessons learned.

EMERGING TECHNOLOGY Sacred Heart Schools completed construction of its Lower and Middle School Library in

August 2012. The library is 6,300 ft2. It has been designed and constructed to achieve Zero

Net Energy (based on site energy), meet the Living Building Challenge and attain LEED

Platinum certification. The project includes many energy efficiency and high performance

features to achieve these goals, including mixed mode natural ventilation design, high

performance glazing, efficient lighting and controls, high thermal resistance cool roof,

variable speed split system conditioning, direct/ indirect evaporative cooling, ceiling fans,

and significant energy sub-metering. The library was originally designed with a natural gas

heated, direct/indirect evaporatively cooled roof top unit (RTU). The design team replaced

this unit in the design phase with an electric heat pump RTU (also with direct/indirect

evaporative cooling) due in part to concerns about meeting the ZNE goals. The original ZNE

projections were based on an optimally tilted PV array, but the final design had the PV

system installed horizontally on the roof which reduced PV output (refer to the PV section

below for more detail). To compensate for the reduced PV output, the more efficient (from a

site-energy perspective) electric heat pump unit was specified.

Due in part to design team concerns about meeting the ZNE goals, the gas heated RTU was

replaced with an electric heat pump RTU (also with direct/indirect evaporative cooling). Key

energy efficient and renewable energy system details are provided below.

The library consists of a main reading room with library stacks, and six smaller rooms

(librarian’s office, a classroom/meeting room, a technology/media storage room, technology

coordinator’s office, tutoring room, and tutoring office). The library also contains two single

occupant staff bathrooms, a janitor closet, a storage closet, an electrical room, and a

mechanical room for the rainwater and graywater system equipment. The floor plan is

shown in Figure 4.

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FIGURE 4: STEVEN’S LIBRARY FLOOR PLAN

The library is typically open between 7am-4:30pm weekdays during the school year. Peak

occupancy is typically around 30 students, although up to 50 students may be present

during standardized testing. The librarian’s estimate of total daily occupancy is between 75

to 100 students per day. Some minor weekend occupancy occurs for meetings. The school

year typically runs from August 28 through December 21 (fall semester) and January 8

through June 7 (spring semester). Refer to Appendix C for the detailed academic calendar

during the M&V period. The library receives minimal student use during the summer, but is

open and used by staff.

The library is part of a larger campus expansion project that included a number of other

buildings. The library does not have its own utility meter, but is served by a campus master

meter.

The project has received technical assistance from Pacific Gas and Electric Company (PG&E)

to evaluate how well it met its ZNE design goals. PG&E has contracted the Cadmus Group to

provide Measurement and Verification (M&V) services. Initial M&V was provided for October

through December 2012 through the PG&E ZNE Pilot Program. Additional M&V was been

provided through the PG&E Emerging Technologies Program to extend the M&V period

through September 2014.

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PV SYSTEM The library has a 40 kWDC rated, grid-connected Photovoltaic (PV) array. The analysis

performed by the design team for the LEED submittal assumed a 0.770 de-rate factor

(accounting for inverter losses, collector soiling, and other losses) and a design AC power

output of 30.8 kWAC. Figure 3 and Figure 5 illustrate the rooftop PV arrangement. The

collectors take up most of the available roof area, once required fire and mechanical access

is accounted for. Note that the LEED analysis in PV Watts assumes the PV panels are tilted

to the South at a 30o angle. During design, the PV system was changed from a tilted

installation to being installed flat on the roof. Installing the collectors flat reduces their

power output. Refer to the Results Section, “Detailed System Performance Analysis” for

detailed discussion and analysis of power output and impacts of the changed PV array

installation angle.

FIGURE 5: PV LAYOUT (FROM DRAWING E401B)

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INDIRECT EVAPORATIVE COOLER/HYBRID HEAT PUMP RTU

(EC1) The main library reading area is served by a single Speakman direct/indirect evaporative

cooler/heat pump roof top unit (RTU) (unit EC1). Note this replaces two earlier specified

Coolerado units with natural gas duct heaters. Figure 6 summarize the RTU technical

specifications. The unit has 5 tons of cooling capacity with an energy efficiency ratio (EER)

of 22. It has a 1.5 HP fan with an efficient electronically commutated motor (ECM)11. The

fan has a 2100 CFM maximum airflow capacity and can be modulated down to 450 CFM via

the ECM. The system uses a demand controlled ventilation (DCV) strategy that monitors

space CO2 levels and automatically adjusts airflow to provide enough ventilation air for

varying occupant loads. Heating is provided by a heat pump with a 46 MBH capacity and

COP of 3.9. There is 5 kW of supplemental electric resistance heating.

FIGURE 6: EC1 EVAPORATIVE COOLER SCHEDULE (FROM DRAWING MSK006)

Figure 7 shows the sequence of operations and control points for EC1. The key energy

efficiency control strategies incorporated in the sequence of operations include night

temperature setback, economizer, night purge, and demand controlled ventilation.

11 An ECM is a high efficiency brushless DC motor with permanent magnets and a built in

inverter. It is more energy efficient than traditional AC motor.

18


FIGURE 7: EC1 EVAPORATIVE COOLING SYSTEM BMS POINTS AND SEQUENCE OF OPERATIONS (FROM DRAWING M602B)

19


PACKAGED HEAT PUMP UNITS The study rooms and classrooms are served by high efficiency variable speed heat pump

units. Specifications for these units are summarized in Figure 8. Figure 9 shows the controls

diagram, which indicates BMS control points and the sequence of operations. The units have

a Seasonal Energy Efficiency Ratio (SEER)12 of 15 to 16.5, and a Heating Seasonal

Performance Factor (HSPF)13 of 8.6 to 9. Each unit is controlled by its own internal controls

and a zonal thermostat. The BMS only has enable/disable control (i.e., scheduling) over

these units, along with space temperature setpoint control.

Split Heat Pump Indoor Fan Coil Schedule

Outdoor Condensing Unit Schedule

FIGURE 8: SPLIT SYSTEM MECHANICAL SCHEDULE

12 The SEER is the ratio of the cooling output during a typical cooling-season (in BTUs)

divided by the total electric energy input during the same period (in Watt-hours). The higher

the unit's SEER rating the more energy efficient it is. 13 The HSPF is the ratio of BTU heat output over the heating season to watt-hours of

electricity used, and it has units of BTU/watt-hr. The higher the HSPF rating of a unit, the

more energy efficient it is.

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FIGURE 9: SPLIT SYSTEM HEAT PUMP BMS POINTS, AND SEQUENCE OF OPERATIONS

EXHAUST FANS The exhaust fan details are summarized in Figure 10. The exhaust fan has a capacity of 350

CFM and a 0.167 HP motor. The exhaust fan consumes minimal energy; energy

consumption for the exhaust fan was not directly measured due to space constraints in the

electrical panel, but was included in total panel logged data. Figure 11shows the exhaust fan

controls drawing. The exhaust fan is controlled by the BMS and scheduled to run when the

main HVAC unit (EC1) is on.

FIGURE 10: EXHAUST MECHANICAL SCHEDULE

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FIGURE 11: EXHAUST FAN CONTROLS

LIGHTING Figure 12 illustrates the lighting layout. Lighting in the main reading area is through

direct/indirect linear fluorescent fixtures with F54T5HO lamps (the long strips in Figure 12,

fixture type F2 and F3) and has daylighting control. The daylighting control system uses

multi-level switching via a Wattstopper LCO-203 Daylighting Control Module. The front part

of the reading room has 16 recessed CFL fixtures (fixture type C2 in Figure 12), with two 32

W triple tube CFL lamps and a total fixture power of 69 W. Lighting in the tutoring and

other rooms is by linear fluorescent fixtures with F32T8 lamps and daylighting and

occupancy sensor controls.

Lighting is wired into its own subpanel, lighting control panel (LCP) B-H1. There is an

electric submeter on this panel that tracks lighting power via the BMS. Note that the current

transducer (CT) installed on the submeter is the wrong size and reads incorrect data (the

submeter is programmed for a specific size CT and its corresponding calibration factor). This

also impacts the total energy use (kWh) reported on the BMS system, as the BMS total

building energy use is the sum of the individual submeter data. Cadmus used temporary

data loggers to obtain actual lighting power data and develop a calibration curve to correct

the reading during the initial M&V period (October – December 2012). Refer to the

“Results” section, “Lighting” and “Data Validation and Quality Control” sections for a

detailed discussion and data. The facility was notified of this issue in the November 2012

monthly report. It was Cadmus’ understanding that the lighting CT was to be replaced with

the appropriate CT. However, upon quality control analysis for the final report (November

2014), it was found that CT for the lighting power submeter had not been corrected. All

lighting power and total building power data presented in this report is corrected using the

updated calibration factor.

22


FIGURE 12: LIGHTING LAYOUT (FROM DRAWING E201B)

DHW The building is equipped with three tankless electric water heaters, with technical specs

summarized in Figure 13. EWH-1 has an 8 kW power rating and serves the bathrooms, and

EWH-2A and EWH-2B have 8.32 kW power ratings and serve the janitor closet.

23


FIGURE 13: DHW SPECIFICATIONS (FROM DRAWING 501B)

RAINWATER AND GRAYWATER SYSTEMS The school is equipped with both rainwater and graywater recovery systems. The rainwater

system details are shown below in Figure 14. Gutters direct rainwater from the roof into a

rainwater storage tank. Water is pumped from the tank, through a self-flushing filter,

through a flow meter and out to irrigate the “eco orchard” via drip irrigation. The rainwater

irrigation system is controlled by the irrigation controller, in conjunction with a water level

sensor located in the rainwater collection tank and a pump controller. The system can also

be controlled via the BMS. Excess rainwater overflows to the storm drain system.

FIGURE 14: RAINWATER LINE DIAGRAM

24


The graywater recovery system consists of both a graywater irrigation system, and a

potable graywater reuse system. The graywater irrigation system is shown in Figure 15, and

the potable graywater system is shown in Figure 16. Incoming graywater from site buildings

(including other nearby buildings constructed at the same time as the library as part of a

larger campus construction project) is collected in a 5,000 gallon graywater recovery tank.

Well water supplements the graywater when needed based on tank level. Overflow goes to

the sanitary sewer. An ozone generator supplies ozone to the tank for treatment.

For the graywater irrigation system, water is pumped from the graywater tank, through a

self-flushing filter, through a flow meter and to the landscape via subsurface irrigation. The

system is controlled by the irrigation controller and BMS.

FIGURE 15: GRAYWATER IRRIGATION 1-LINE DIAGRAM

For the potable graywater reuse system, graywater is pumped from the graywater tank,

through a sand filter, 5 micron, 1 micron filter, and then a UV filter. The system is kept

pressurized, and supplies the library’s toilets. Domestic water supplements the system if

needed.

25


FIGURE 16: GRAYWATER POTABLE 1-LINE DIAGRAM

An electric submeter is installed on the feed to the rainwater and graywater systems and is

monitored by the BMS system.

BUILDING MANAGEMENT SYSTEM (BMS)

The library is controlled by a Delta Controls BMS system, with a front end accessible online.

Control points and sequences for key equipment are listed above for each system. Cadmus

worked with the controls contractor to set up trend logs that would be valuable to M&V and

provide useful data to facility personnel tasked with long term operations and maintenance

of the building’s ZNE performance.

LUCID DESIGNS BUILDING DASHBOARD The building also has a Lucid Designs building dashboard. The dashboard uses a stand-along

web-server. This web server pulls trend data from the BMS system and then processes and

stores the data internally to serve the dashboard. Cadmus originally anticipated it would be

utilize the data from the dashboard’s webserver. However, the dashboard was not

completed in time for M&V use. The controls contractor was able to provide the M&V team

with access to the underlying BMS system and programmed the needed trend logs for M&V

use. The building dashboard is currently operational, but the M&V team does not have

access.

26


ASSESSMENT OBJECTIVES The objectives of this project are to:

1. Document whole building and end-use energy consumption for a new ZNE building,

the Sacred Heart Schools Stevens Library;

2. Conduct an operational survey of building occupants and staff to obtain space usage

data to correlate with M&V data, and to identify any related issues that affect

performance or comfort (e.g., temperature control problems);

3. Recalibrate the original design building energy model to actual conditions;

4. Compare modeled vs. actual building energy performance; and,

5. Identify issues, challenges, problems, and opportunities to inform and guide design

of future ZNE buildings.

TECHNOLOGY EVALUATION Cadmus monitored whole building and end use energy consumption and onsite PV

generation for the 6,300 ft2 Sacred Heart Schools Lower and Middle School Library,

designed to be ZNE, over nearly two years of operation. The library is all-electric.

Key end uses monitored include lighting, water heating, space conditioning, plug loads and

miscellaneous, and a grey water and rainwater catchment system. Furthermore, additional

trend logs were set up on the BMS, and additional temporary loggers were installed on

specific pieces of equipment to aid in diagnostics if needed.

M&V was conducted in two discrete phases and provided performance data for a nearly two

year period from December 2012 through September 2014.

The monitored data was used to determine whether the library achieved its ZNE design

goals. The design model was recalibrated to represent existing conditions. The recalibrated

model was used to help aid assessment of how accurate the original energy model was, and

to help assess how well the building was doing in meeting its ZNE goals over the course of

the M&V period. An occupant survey was also conducted to identify user satisfaction with

the building and identify issues that might affect building performance or be indicators of

equipment problems.

TECHNICAL APPROACH

FIELD TESTING OF TECHNOLOGY The project began with the creation of a detailed M&V plan developed in close coordination

with Sacred Heart Schools, PG&E, the architect, the MEP engineer (who also performed the

27


LEED energy modeling), the controls contractor, the commissioning agent, and other key

members of the design and construction team. This M&V Plan is provided in Appendix C.

Once the M&V plan was finalized, Cadmus worked with the controls contractor to set up

trend logs for key points for the library, including HVAC equipment, ceiling fans, lighting,

the E‐Mon D‐Mon submeters and the PV system.

Temporary data loggers were also installed to obtain additional detailed electricity

performance data on key circuits and equipment (refer to Table 2 under “Instrumentation”

below for details) to aid in diagnostics and to confirm BMS calibrations.

During data logger installation, spot electricity measurements were taken on accessible

pieces of equipment. The BMS submeter installation was also reviewed (e.g., CT sizes and

placement, submeter CT setpoints).

Initial M&V was conducted from October through December 2012. An additional year of M&V

was later authorized for September 2013 through September 2014. The temporary data

loggers were left in place during the interim period of October 2013 through August 2013

with the hopes that this data would be available. Unfortunately some of the loggers’

batteries died, and the loggers ran out of memory, so this data is not as complete as

desired. The Dent data loggers used for the initial M&V period required monthly physical

downloading. To facilitate data access for QC purposes and minimize the need to access

customer electric panels (mitigating risk), Cadmus replaced the original Dent data loggers

with Hobo data loggers equipped with cellular communications. Note that due to physical

space constraints inside the electrical cabinet a new temporary data logger could not be

installed on the feed to the lighting subpanel (panel B-H1). This was not deemed to be an

issue as this panel was already being logged by the BMS, and Cadmus was not yet aware

that the mis-sized CT on this panel identified in the initial M&V period had not been

replaced.

The BMS trend logs were able to provide a continuous record of performance. This M&V

report contains performance data for a nearly two year period from December 2012 through

September 2014. Note that October and November 2012 data is available from the initial

M&V period, but these initial occupancy months were atypical and not included in the final

reported energy consumption data as the building was still undergoing final commissioning,

the BMS programming was being fine-tuned, and related initial occupancy issues were being

addressed.

During the M&V period, monthly reports were submitted to and reviewed with PG&E. The

monthly reports tracked performance and identified issues that needed to be addressed.

An occupancy survey was administered to the library’s staff to observe and document use

patterns, system performance, and related data. The purpose of this survey is to obtain

space usage data to correlate with M&V data, identify any related issues that affect

performance or comfort (e.g., temperature control problems), and to assist in the building

energy model calibration.

The design team’s building energy simulation model developed for the LEED submittal was

reviewed and calibrated using meter and BAS data along with operational survey results for

the initial M&V period of October through December 2012. The energy model was

recalibrated at the end of September 2014 using the additional M&V data.

28


INSTRUMENTATION PLAN The M&V activities were designed to leverage the school’s existing metering and the Delta

Controls BMS to the greatest extent possible. The BMS provides rich performance and

operational data necessary to track and diagnose system performance and keep the building

on track to maintain ZNE for years to come. It is hoped that this project will serve as a

template to assist the school’s facilities personnel to continue tracking long term

performance. BMS trend data was downloaded monthly and on an as-needed basis (to aid in

diagnostics) via remote read-only log-in. Table 1 summarizes the primary and supplemental

BMS trend data used in the M&V project. The primary points are the electricity submeter

data that was relied upon for energy tracking and which were tracked on a monthly basis.

The supplemental points are additional points that were reviewed as needed to interpret

performance data, confirm correct operation, and diagnose issues.

TABLE 1: BMS TREND DATA POINTS

TREND POINT NAME UNIT PRIMARY/

SECONDARY NOTES

Total Building Electricity Submeter kWh Primary

E-Mon D-Mon system connected to BMS. Hourly averaging intervals on trend logs.

Electric Water Heating Submeter kWh Primary

E-Mon D-Mon system connected to BMS. Hourly averaging intervals on trend logs.

Lighting Panel Submeter kWh Primary E-Mon D-Mon system connected to BMS. Hourly averaging intervals on trend logs.

Water Heating Submeter kWh Primary E-Mon D-Mon system connected to BMS. Hourly averaging intervals on trend logs.

Plug And Misc. Loads Submeter kWh Primary

E-Mon D-Mon system connected to BMS. Hourly averaging intervals on trend logs

PV Output kWh Primary

Provided to BMS by Digital communication

card on inverter. “Change of value” recording interval.

PV Power kW Primary

Provided to BMS by Digital communication

card on inverter. “Change of value” recording interval.

Room 201 Tutoring Room Temp oF Secondary

Room 204 Tech Room Temp oF Secondary

Electrical Room Temp oF Secondary

Room 207 Meeting Room Temp oF Secondary

Room 211 Office Room Temp oF Secondary

Supply Air Temp East oF Secondary EC1 Multi-trend

Supply Air Temp West oF Secondary EC1 Multi-trend

West Room Temp oF Secondary EC1 Multi-trend

Incoming Air Temp oF Secondary EC1 Multi-trend

29


East Room Temp oF Secondary EC1 Multi-trend

East CO2 Sensor CO2 PPM Secondary EC1 Multi-trend

West CO2 Sensor CO2 PPM Secondary EC1 Multi-trend

EC-1 Compressor Status On/Off Secondary Main RTU Unit; EC2 Multi-trend

EC-1 Fan Status On/Off Secondary Main RTU Unit; EC2 Multi-trend

EC-1 Direct Evap. Cooling Pump Status On/Off Secondary Main RTU Unit; EC2 Multi-trend

EC-1 Indirect Evap Cooling Pump Status On/Off Secondary Main RTU Unit; EC2 Multi-trend

Total Water Usage Gal Secondary

Irrigation Graywater Usage Gal Secondary

Domestic Water Usage Toilets Gal Secondary

Well water Makeup Usage Gal Secondary

Bldg. Graywater Usage Gal Secondary

Rainwater Usage Gal Secondary

In addition to the BMS, an array of portable Dent Elite-pro power meters/data loggers was

installed in the electrical panels to measure individual equipment and loads. These were

installed for two primary purposes: (1) during M&V plan development it was not clear how

much data would be available on the BMS and its utility for M&V, and (2) the additional

temporary meters provide a means to cross check BMS electric submeter data, and provide

additional data to diagnose and interpret building operational issues if needed.

Key Dent ElitePro Power Meter/Data Logger specifications are summarized below.

Measurement Type: True RMS using high - speed digital signal processing (DSP)

Waveform Sampling: 12 kHz

Channel Sampling Rate (internal sampling): 200 samples /cycle at 60Hz

Data Interval: The default integration period (used here) is fifteen minutes.

Accuracy: Better than 1% (<0.5% typical) for V, A, kW, kVAR, kVA, PF

Resolution: 0.01 Amp, 0.1 Volt, 0.1 Watt, 0.1 VAR, 0.1 VA, 0.01 Power Factor

Cadmus installed the Dent dataloggers in the Stevens Library on October 25, 2012. Trained

metering specialists deployed the meters. Prior to meter deployment, all meters were

programmed, appropriately sized CTs connected, electrical configurations programmed,

batteries charged, and logger operation confirmed. Table 2 summarizes the temporary data

logger locations and circuit details. It also documents key spot measurements that were

made during logger deployment. Table 3 documents data logger details, including CT size,

meter ID, and available memory. The loggers recorded 15 minute integrated interval data.

30


TABLE 2: DATA LOGGER AND SPOT MEASUREMENT INSTALLATION SUMMARY

PANEL DESCRIPTION CIRCUIT

MEASURE

MENT

UNIT NOTES

B-L1

Total Building Electricity - Entire Panel B-L1 Feed

Panel

Feed kW

Exhaust Fan: EF-1B 9

Amps Spot measurement of 1 Amp BMS provides on/off data

Speakman Unit 1, 3 Amps

East duct heater 23, 25 Amps

West duct heater 5, 7 Amps

Split system units: CU-1B/3B, FC-1B, FC-3B 11, 13 Amps

Split system units: CU-2B/FC-2B 15, 17 Amps

Split system units: CU-4B/5B, FC-4B, FC-5B 19, 21 Amps

Electric water heaters 2, 4, 6,

8, 10, 12

Amps

R-Ozone generator 14 Amps

Pump P-2 16 Amps

Drainage Pump

20

Amps Drainage Pump was not

installed (found after installation)

UV Filter 18 Amps

Irrigation/graywater pump 24, 26 Amps

Rainwater Irrigation Pump 29 Amps

B-L2 Panel B-L2 Feed (plug loads, ceiling fan, misc.)

1, 3 kW kW

B-H1

Ltg - Reading Room Fluorescent Pendants Panel Feed kWh kWh

Ltg - Reading Room Downlights & Nook Panel

Feed kWh kWh

Ltg - Exterior Above Doors Panel

Feed kWh kWh

31


TABLE 3: DATA LOGGER CONFIGURATION DETAILS

At the end of the initial M&V period (December 2012) it was anticipated that the M&V would

be extended, and therefore the Dent dataloggers were left in situ. The second M&V phase

took effect in October 2013. The Dent loggers were downloaded. Some of the loggers

experienced battery failure and lost their data. The loggers that were still in operation had

run out of memory and had overwritten earlier data. Fortunately, a continuous record of the

BMS trend points, used as the primary M&V data source, was available during this period.

New Hobo U30 data loggers14 with cellular communications and Wattnode kWh

transducers15 were installed to replace the Dent dataloggers. The remote access capabilities

improved team access to the data and eliminated to eliminate the need for routine opening

of the electrical panels to access and download the data loggers. This provides improved

14 http://www.onsetcomp.com/products/data-loggers/U30-data-loggers 15 http://www.onsetcomp.com/products/sensors/t-wnb-3d-480

Logger ID

Installation

Location

Installation/ Panel

Location Circuit to be logged CT Desc

Expected

Max Amps CT Amps

Max

Logging

Duration

Ph A 342 500 55 days

Ph B 292 500 55 days

Ph C 307 500 55 days

Ph A 17 500 55 days

Ph B 7 500 55 days

Ph C 1 500 55 days

Ckt 37: Ph A 96 150 55 days

Ckt 39: Ph B 78 150 55 days

Ckt 41: Ph C 90 150 55 days

ckt 1 40 50 77 days

ckt 3 40 50 77 days

ckt 5 <50 50 77 days

ckt 7 <50 50 77 days

ckt 23 <50 50 77 days

ckt 25 <50 50 77 days

ckt 11 <50 50 77 days

ckt 13 <50 50 77 days

ckt 15 <50 50 77 days

ckt 17 <50 50 77 days

Ckt 19 <50 50 77 days

ckt 21 <50 50 77 days

ckt 2 <50 50 77 days

ckt 4 <50 50 77 days

ckt 6 <50 50 77 days

ckt 10 <50 50 77 days

ckt 14 - Ozone <50 50 55 days

ckt 16 Pmp P2 <50 50 55 days

ckt 18 UV Filter <50 50 55 days

ckt1 - Clg fans <50 50 77 days

ckt 3 - clg fans <50 50 77 days

DL26Electrical Room from B-L1 EWH2a/2b

DL-1Electrical Room from B-L1 CU2b/FC2b

DL15Electrical Room from B-L1 CU 4b/58

DL13Electrical Room from B-L1 EWH1

West Duct Heater

Speakman UnitDL24

from B-L1

DL2Electrical Room from B-L1 East Duct Heater

DL3Electrical Room from B-L1

Split HP CU1b 3b & FC

1b 3b

Feed to B-L1

Feed to B-H1

(alternately can be

main bldg feed if

From panel B-L1

Electrical Room

Electrical Room

Electrical Room

from B-L1DL7

Electrical Room

Electrical Room

DL10

Electrical Room From B-L1Graywater/Rainwate

r Equipment

DL18Electrical Room from B-L2 Ceiling Fans

DL8

DL6

DL19

Panel B-H1 Feed (or

main bldg feed)

Panel B-L1 feed

Panel B-L2 feed

http://www.onsetcomp.com/products/data-loggers/U30-data-loggers

http://www.onsetcomp.com/products/sensors/t-wnb-3d-480

32


risk management. Specifications are similar to the Dent specifications. All meters were

installed by Cadmus’ metering specialists.

RESULTS Key results of the Stevens Library M&V activities are reported here. Data is reported for the

period of 12/1/2012 through 9/30/2014, for which a complete and consistent set of energy

consumption data is available. Data for October and November 2012 is not displayed. Due

to building startup, occupant move-in, BMS programming, and related issues, there are

some data inconsistencies and the data is not representative of typical building use. Also

note that per discussion under the Data Validation section, the BMS’s reported lighting

power is incorrect due to a CT mismatch on the submeter. The BMS also incorrectly reports

the total facility electricity use, which is the sum of all the submeters. All of the data

presented below in the Data Analysis section use corrected values for both lighting and total

consumption.

DATA ANALYSIS

ENERGY BALANCE

The building is exceeding its ZNE performance goals by a significant margin. For 2013 the

PV system generated 54% more electricity than the building consumed. The trend is similar

for 2014. For the entire monitoring period (12/12 – 9/14), the PV system has generated

73% more electricity than the building has consumed.

Figure 17 plots the monthly building electricity use, PV generation and net energy, and

year-to-date (YTD) cumulative net energy16. There is significant excess solar energy

generation during the summer. This is due to a combination of reduced summer occupancy

and increased solar generation. The building is very efficient and has an energy use

intensity (EUI) of 18.5 ± 0.5 kBTU/ ft2/year17. This is significantly lower than the New

Building Institute (NBI) “Getting to Zero” Report18’s definition of “ZNE Capable” buildings19.

lots the monthly energy balance of the facility. This graph summarizes building ZNE

performance. Electricity used by the building is plotted on blue and electricity generated by

the PV system in red (both read on the left axis). Note that for February 2013 through

16 Net energy = PV generation – building use; positive means net electricity generation,

negative means net electricity use. 17 Average annual EUI based on the entire M&V period data from 12/12 – 9/14. Note that the

2013 EUI is 19.0 kBTU/ft2. The uncertainty is the standard deviation between 2013 data and

the extrapolated 2014 EUI data based on 1/2014 through 9/14 data. 18 New Buildings Institute. “Getting to Zero 2012 Status Update: A First Look at the Costs

and Features of Zero Energy Commercial Buildings.” March 2012.

http://newbuildings.org/sites/default/files/GettingtoZeroReport_0.pdf 19 NBI defines ZNE Capable buildings as buildings with EUIs ≤ 35 kBTU/ft2/year. They do not

differentiate by use type.


33


December 2013 the PV generation is estimated due to a failure in the inverter

communication card that reports PV generation data to the BMS for trending. Data is

estimated with a high degree of confidence using a correlation discussed under the Data

Validation section. The year-to-date (YTD) cumulative net energy is plotted in green and

blue (filled) on the right axis. During the winter, the facility uses more electricity than it

generates. The YTD net energy starts the year in deficit. However, heating energy drops

and PV production increases for the rest of the year, resulting in an overall positive energy

balance. The building is healthily zero net energy.

FIGURE 17: MONTHLY ENERGY BALANCE AND YEAR-TO-DATE CUMULATIVE NET ENERGY

Figure 18 compares 2013 and 2014 monthly electricity consumption. As discussed in more

detail in the “Detailed System Performance Analysis” section, the differences are explained

by the following factors: 2013 has more heating degree days that 2014, which increases

2013 winter heating energy. In 2014, summer lighting power increases (no summer

reduction in lighting electricity is observed as per 2013) and plug loads trend upward.

-5,000

0

5,000

10,000

15,000

20,000

25,000

30,000

-2,000

0

2,000

4,000

6,000

8,000

10,000

12,000

YTD

Cu

mu

lati

ve N

et

Ene

rgy

(kW

h)

Mo

nth

ly E

ne

rgy

(kW

h)




PV Generation

Estimated PV Generation

34


FIGURE 18: 2013 VS 2014 MONTHLY LIBRARY ELECTRICITY USE

The net energy balance for 2013, 2014 (partial year) and entire M&V period is summarized

in Figure 19. The building is a net electricity generator (i.e., “net positive”) for 2013,

January through September 2014 (and should be net zero for all of 2014), and for the

entire M&V period (12/12 through 9/14). Note that PV electricity generation for the first

nine months of 2014 is almost the same as all of 2013. Refer to the “Detailed System

Performance Analysis/PV System” section below for additional discussion. The building is

FIGURE 19: NET ENERGY BALANCE

-

1,000

2,000

3,000

4,000

5,000

6,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

kWh 2013

2014

-

20,000

40,000

60,000

80,000

100,000

120,000

2013 2014 (partialyear, 1/14 -

9/14)

All Data (12/12 -9/14)

An

nu

al E

lect

rici

ty (

kWh

)


PV Generation

Net Energy

35


Table 4 provides actual monthly energy performance data. Refer to the preceding

paragraphs for relevant discussion.

TABLE 4: MONTHLY BUILDING ELECTRICITY CONSUMPTION AND GENERATION DATA

MONTH

BUILDING

ELECTRICITY

CONSUMPTION

(KWH)

PV GENERATION

(KWH) (ESTIMATED

DATA 2/13 -

12/13)

MONTHLY NET

ENERGY (KWH;

NOTE: + VALUES =

MORE GENERATED

THAN USED)

12/12 2,934 1,796 (1,138)

1/13 5,419 2,576 (2,843)

2/13 5,274 2,966 (2,309)

3/13 3,115 4,246 1,131

4/13 1,996 5,337 3,341

5/13 2,244 6,645 4,401

6/13 1,675 6,198 4,523

7/13 1,648 6,703 5,055

8/13 1,800 5,956 4,156

9/13 2,318 4,756 2,438

10/13 2,401 3,678 1,277

11/13 2,774 2,589 (185)

12/13 4,458 2,289 (2,168)

1/14 4,713 2,651 (2,062)

2/14 3,703 2,732 (972)

3/14 2,590 5,042 2,451

4/14 1,948 6,662 4,714

5/14 1,891 7,953 6,061

6/14 1,850 8,025 6,175

7/14 2,530 7,450 4,920

8/14 2,647 6,536 3,889

9/14 2,713 5,002 2,289

Table 6 summarizes 2013 annual, 2014 year to date, and total M&V period building energy

consumption and PV generation. The Library is a net energy generator on an annual basis.

Note that PV production for 2014 through September is almost equal to total 2013 PV

generation.

36


TABLE 5: ANNUAL AND M&V PERIOD SUMMARIES OF BUILDING ELECTRICITY CONSUMPTION AND GENERATION DATA

MONTH

BUILDING

ELECTRICITY

CONSUMPTION

(KWH)

PV GENERATION

(KWH) (ESTIMATED

DATA 2/13 -

12/13)

MONTHLY NET

ENERGY (KWH;

NOTE: + VALUES =

MORE GENERATED

THAN USED)

2013 Total 35,123 53,939 18,816

2014(partialyear,1/14-9/14)

24,586 52,052 27,466

All Data(12/12-9/14) 62,644 107,787 45,144

ENERGY END USES

Figure 21 summarizes the overall energy end use distribution for 2013 (for which there is

complete data for an entire calendar year). Space conditioning consumes just over 60% of

the building’s energy and is the biggest load. Of the space conditioning load, fans account

for 32%; heating for 48%; cooling for 15%; and pumps, controls, and other ancillary loads

for the remaining 4%. Lighting and non-regulated loads primarily under occupant control

(e.g., plug loads and ceiling fans) consume equal amounts at ~15% of the total building

load each. The rainwater and graywater collection system consumes 8% of the building

energy. The rainwater and graywater loads include a variety of pumps, UV sterilizers and

related equipment—refer to the Emerging Technology section for equipment details.

FIGURE 20: 2013 ANNUAL ENERGY END USE DISTRIBUTION

Space Conditioning, 21,367 , 61%

Lighting, 5,211 , 15%

Water Heating, 80 ,

0.2%

Rainwater/ Graywater

System, 2,750 , 8%

Ceiling Fans and Plug

Loads (Panel B-L2), 5,715 ,

16%

37


Monthly end use breakouts are shown in Figure 21

FIGURE 21: MONTHLY ENERGY END USE DISTRIBUTION

ENERGY USE INTENSITY

A building’s Energy Use Intensity (EUI) is the amount of annual energy a building uses on a

square foot basis, with units of kBTU/ft2/year. During the M&V period, Stevens Library had

an EUI of 18.5 ± 0.5 kBTU/ft2/year20. Note that the 2013 EUI is 19.0 kBTU/ft2. The

uncertainty is the standard deviation between 2013 data and the extrapolated 2014 EUI

data based on 1/2014 through 9/14 data.

BENCHMARKED PERFORMANCE Figure 22 shows the adapted EUI scale from the New Building Institute (NBI)’s “Getting to

Zero” Report21 with the Library’s EUI indicated. NBI defines “ZNE Capable” buildings as

those with EUIs ≤ 35 kBTU/ft2/year. This is the upper limit of actual ZNE building energy

use (excluding renewables generation) that is in NBI’s ZNE building database. Clearly, the

Library’s EUI of 18.5 ± 0.5 kBTU/ft2/year is a very high performing building and one the

School and design team should be proud of.

20 the average annual EUI based on 12/12 – 9/14 data. 21 New Buildings Institute. “Getting to Zero 2012 Status Update: A First Look at the Costs

and Features of Zero Energy Commercial Buildings.” March 2012.


-

1,000

2,000

3,000

4,000

5,000

6,000

12

/12

1/1

3

2/1

3

3/1

3

4/1

3

5/1

3

6/1

3

7/1

3

8/1

3

9/1

3

10

/13

11

/13

12

/13

1/1

4

2/1

4

3/1

4

4/1

4

5/1

4

6/1

4

7/1

4

8/1

4

9/1

4

Mo

nth

ly E

lect

rici

ty (

kWh

)

Rainwater/ GraywaterSystem

Space Conditioning

Water Heating

Lighting

Ceiling Fans and Plug Loads(Panel B-L2)


38


FIGURE 22: STEVENS LIBRARY BENCHMARKED AGAINST ZNE BEST PRACTICE GUIDELINES

The Library’s annual EUI (based on the entire M&V period data of 12/2012 – 9/2014) is also

benchmarked using the Lawrence Berkeley National Laboratory’s EnergyIQ benchmarking

tool (http://energyiq.lbl.gov/EnergyIQ/SupportPages/EIQ-about.jsp). Note that while The

library does not fit neatly into the predefined building use types, it is still of some use to

compare it to the nearest building types. Figure 23 through Figure 25 benchmark the

Library against three different peer groups (all schools, elementary and middle/secondary

schools only, and all office and schools) in the central coast region for all vintages of

buildings. Unfortunately, there is insufficient data to further narrow down the peer group by

vintage and other factors. All data is based on California’s Commercial End Use Study

(CEUS). Note that in the first two cases, the peer group is quite small (18 and 9 buildings

respectively), and that there is a wide variation in peer group EUI.

Figure 23 shows the distribution of energy use for school buildings in California’s central

coast (including the Bay Area) for all school buildings of all vintages and sizes. The median

EUI for these buildings is 24.3 kBTU/ft2/year, with a range of 13.5 to 52.1 kBTU/ft2/year

(5th to 95th percentiles). There are only 18 buildings in this category. The data is from

California’s Commercial Energy End Use Survey (CEUS), for all school types (pre-school

through secondary school), for all vintages. Insufficient data is available to refine the results

SHS LMS

Library (18.5

± 0.5 kBTU/SF)

http://energyiq.lbl.gov/EnergyIQ/SupportPages/EIQ-about.jsp

39


by vintage. It is interesting to note that there is a significant population of buildings with a

lower EUI of 15 kBTU/ft2/year. These are likely non air-conditioned buildings.

FIGURE 23: ENERGYIQ BENCHMARK DATA FOR ALL CALIFORNIA CENTRAL COAST SCHOOLS

Figure 24 shows similar data, but refined to only show elementary and secondary school

buildings. There are only 9 buildings in this category. The median EUI for these buildings is

38.8 kBTU/ft2/year, with a range of 25.4 to 53.2 kBTU/ft2/year (5th to 95th percentiles).

Unfortunately, there is no descriptive data for the EnergyIQ benchmarked data (i.e.,

occupancy and schedule data such as whether the schools are in use year-round or off

during the summer). It is likely that most of the buildings are for entire schools, not just

school libraries. The use of this data is therefore limited, but it is nevertheless helpful to see

how the Library compares to the closest set of peer buildings for which there is data.

SHS LMS

Library (18.5

± 0.5 kBTU/SF)

40


FIGURE 24: ENERGYIQ BENCHMARK DATA FOR ALL CALIFORNIA CENTRAL COAST ELEMENTARY AND MIDDLE SCHOOLS

For a broader perspective, Figure 25 shows benchmark data for all central coast office and

schools. This provides a more robust sample size of 131 buildings. The typical (median) EUI

for these buildings is 34.0 kBTU/ft2/year, with a range of 14.4 to 87.3 kBTU/ft2/year (5th to

95th percentiles). Note that most offices likely have year-round occupancy, whereas while

Steven’s Library is operational during the summer, it has minimal occupancy. Offices are

therefore expected to have higher EUI.

While these benchmarks have limitations, it does show that Steven’s Library is clearly a high

performing building regardless of the peer group it is compared to. Its use of direct and

indirect evaporative cooling, natural ventilation, low plug loads, efficient envelope, efficient

lighting and controls, and comparatively low operating hours (minimal summer occupancy;

academic calendar breaks and holidays) help it achieve this performance.

SHS LMS Library

(18.5 ± 0.5 kBTU/SF)

41


FIGURE 25: ENERGYIQ BENCHMARK DATA FOR ALL CALIFORNIA CENTRAL COAST OFFICE AND SCHOOL BUILDINGS

OCCUPANT SURVEY

The librarian was surveyed to document use patterns, system performance, and related

data. The purpose of this survey is to obtain space usage data to correlate with M&V data,

and to identify any related issues that affect performance or comfort (e.g., temperature

control problems). Key results are summarized below. The full survey and responses is

provided in the appendix.

OPERATING HOURS AND OCCUPANCY

The library is typically occupied between 7am-4:30pm on weekdays. The librarian typically

arrives at 7 am and departs at 4:30 pm, and the library is open to students per the

following schedule:

Monday: 7:30-3:30pm

Tuesday-Thursday: 7:30 – 4pm

Friday: 7:30-3:30pm

There is some weekend use of the space for meetings and other school events. However,

this is not intensive, occurring approximately 1 day per month.

The typical occupancy and use patterns for the library are as follows:

The best estimate of total patrons (students) visiting the library throughout the day

is 75-100 people per day.

SHS LMS Library

(18.5 ± 0.5 kBTU/SF)

42


The Librarian has ~ 14 classes/week using the library. Typical classes have 18

students and each class spends 15-30 minutes in the library.

Some students use the facility during lunch (12pm-1pm). During standardized

testing, there were approximately 50 students occupying the main library. However

this is infrequent. Aside from this, the maximum number of library visitors at any

one time is estimated at around 30 people.

The typical occupancy and use of the classrooms, tutoring rooms and other rooms are as

follows:

Tutoring Room (Rm 203): 2 people

Conference Room: When occupied, typically 2-4 people.

Tech Office: 2 employees, students visit throughout the day for computer servicing.

Students come to the tech office for computer support. This occurs throughout the

day, heavy traffic occurs during lunch

Next to Tech Office: 2 employees

The custodial staff cleans the library once daily. The specific timing of the cleaning is

variable.

TEMPERATURES

Occupants report that the library temperatures are generally satisfactory, except during the

winter when the mornings are cold. Space temperatures are typically comfortable by around

8:20 am. The space is reported to generally warm up throughout the day. The main RTU

serving the library was warrantied due to the heat pump locking out. It is believed that this

has helped address the problems. The space temperature was reported by one librarian as

never being too hot. However, another reported that in the summer when the sun is out,

that the space can be a bit warm.

When there are a number of people occupying the library at a given time (i.e. standardized

testing), interviewees feel that there is insufficient outside air and the HVAC system has

trouble maintaining space temperature. However, it is reported that the space is generally

able to maintain temperature setpoints during large occupancy changes.

The temperatures in the smaller classrooms and tutoring rooms are generally comfortable,

although there are times when the space gets “stuffy”. Occupants typically open windows

when this occurs. Note that there are no window interlock switches that automatically turn

the air-conditioning off when windows are opened, so the air-conditioning will continue to

run unless occupants manually turn it off. The technology coordinator office gets a bit warm

due to the number of electronic equipment in the space. Staff does not complain and

instead opens windows so the room feels uncomfortable.

VENTILATION AND FRESH AIR

Occupants have made consistent remarks regarding perceived air quality. Occupants feel

that private offices and main reading room can get stuffy on occasion. It is possible that this

is related to humidity, specifically increased humidity due to the direct evaporative cooling

mode. It should be noted that the main library reading room has CO2 sensors and demand

control ventilation, which appears to be working correctly, without excessive CO2 levels

observed. Also note that there was a warranty repair on EC-1 (the main reading room HVAC

unit) to correct a problem related to compressor heating lockout, which was leading to

problems meeting space temperatures on colder days. It is possible that this issue led to

some occupant feelings of stuffiness in the main reading room. Occupants of private spaces

43


typically leave windows open when they feel stuffy. This can have energy implications if

consistently done with the heating/cooling system on. BMS data does not indicate that this

a major issue at present.

CEILING FANS

The library is equipped with ceiling fans in the main reading room. The ceiling fans are

manually operated. The Librarian only uses these fans when the library is fully occupied.

She feels the fans create a lot of noise.

LIGHTING

The occupants are unaware of dimming controls on the lighting system. During the summer,

occupants report regular glare problems and close the window shades during periods of

glare. Occupants believe the occupancy controls are working as intended. There was some

concern at the start of the project that the ceiling fans may keep the occupancy sensors

from working correctly. However, no problems have been reported.

EQUIPMENT AND PLUG LOADS

Cadmus inventoried the interior equipment and plug loads as part of the occupancy survey.

Equipment and plug loads are summarized in Table 6

TABLE 6: LIBRARY EQUIPMENT AND PLUG LOADS COUNT

Equipment Quantity Notes/Comments desktop computers and monitors 10 laptops - TV’s and additional

screens/monitors 1 in reading room

printers 3 large photocopiers - small/medium photocopiers - refrigerators/freezers - Computer cart/charging station 1 in tech room for laptops

DETAILED SYSTEM PERFORMANCE ANALYSIS Individual system performance was reviewed to ensure that all systems were properly

operating and to identify issues that could negatively impact energy use. All systems were

analyzed in detail at the start of the M&V period. System performance was then reviewed

throughout the project on an as-need basis only as issues arose.

WEATHER

Weather drives a significant portion of the Library’s energy use. Historical weather data for

the M&V period is presented below and referred to throughout the discussion to help

interpret and analyze the results.

44


Historical weather data for the site was obtained for the M&V period from a local weather

station in Atherton (station ID = KCAATHER422). The design phase building energy model

(used to document LEED energy points, California Title 24 compliance, and estimate annual

energy use for ZNE sizing calculations) and the subsequent energy model updates and

calibrations use California Climate Zone 4 climatic data (CACZ04)23. The use of standard

typical meteorological year (TMY) type climatic data such as the California Climate Zone

climatic data is common practice for design teams and energy modelers.

Figure 26 and Table 7 compare CACZ04 climatic data to historical heating degree day (HDD)

data during the M&V period. 2013 has more HDD’s than 2014 and 2014 has more HDDs

then CACZ04 climatic data, particularly for December and January. This leads one to expect

more heating energy in 2013 than 2014, and more heating energy in both 2013 and 2014

than the modeled data which uses the CACZ04 data. Refer to the “Building Energy Model

Evaluation” section for more discussion.

FIGURE 26: HEATING DEGREE DATA DURING THE M&V PERIOD

22 Historical data is available from

http://www.wunderground.com/weatherstation/WXDailyHistory.asp?ID=KCAATHER4&day=1

4&year=2012&month=10&graphspan=month 23 The actual filename used is CZ04RV2

-

100

200

300

400

500

600

HD

D B

ase

18

C/6

5F

CA CZ04

2012

2013

2014

http://www.wunderground.com/weatherstation/WXDailyHistory.asp?ID=KCAATHER4&day=14&year=2012&month=10&graphspan=month

http://www.wunderground.com/weatherstation/WXDailyHistory.asp?ID=KCAATHER4&day=14&year=2012&month=10&graphspan=month

45


TABLE 7: HEATING DEGREE DATA DURING THE M&V PERIOD

Figure 27 and Table 8 compare CACZ04 climatic data to historical heating degree data

(HDD) during the M&V period. 2013 and 2014 are hotter than the CACZ04 climatic data.

This leads one to expect increased air conditioning energy compared to modeled cooling

energy. Also, July and August were significantly hotter than 2014 than in 2013. Thus,

cooling energy should be higher during these months in 2014.

FIGURE 27: COOLING DEGREE DATA DURING THE M&V PERIOD

CA CZ04 2012 2013 2014 2012 2013 2014

January 270 511 361 241 91

February 195 380 298 185 103

March 170 264 190 94 20

April 121 141 169 20 48

May 57 105 68 48 11

June 5 26 29 21 24

July 1 9 - 8 (1)

August - 9 - 9 -

September - 11 5 11 5

October 54 140 86

November 185 233 254 48 69

December 255 513 258

Through September 819 1,120 301

Annual Total 1,313 2,363 1,050

HDD base 18C/5F Difference from CA CZ04

0

20

40

60

80

100

120

140

160

180

CD

D B

ase

18

C/6

5F

CA CZ04

2012

2013

2014

46


TABLE 8: COOLING DEGREE DATA DURING THE M&V PERIOD

Figure 28 summarizes the average daily maximum temperature for each month during the

M&V period and for CACZ04. Average monthly maximum temperatures are very consistent

for all three periods. Interestingly, the peak temperatures are higher for the climatic data

during the summer, although 2013 and 2014 have more CDDs.

FIGURE 28: AVERAGE MAXIMUM MONTHLY TEMPERATURE DURING THE M&V PERIOD

CA CZ04 2012 2013 2014 2012 2013 2014

January 0 0 0 - -

February 0 0 0 - -

March 0 0 0 - -

April 1 36 25 35 24

May 3 58 52 55 49

June 26 111 77 85 51

July 49 93 168 44 119

August 67 124 155 57 88

September 41 120 107 79 66

October 9 29 20

November 0 5 0 5 -

December 0 0 -

Through September 187 584 (235)

Annual Total 196 571 375

CDD base 18C/65F Difference from CA CZ04

-

10

20

30

40

50

60

70

80

90

Tem

pe

ratu

re (

F)

CA CZ04

2013

2014

47


FIGURE 29: AVERAGE MEAN MONTHLY TEMPERATURE DURING THE M&V PERIOD

FIGURE 30: AVERAGE MINIMUM MONTHLY TEMPERATURE DURING THE M&V PERIOD

-

10

20

30

40

50

60

70

80

90

Tem

pe

ratu

re (

F)

CA CZ04

2013

2014

-

10

20

30

40

50

60

70

80

90

Tem

pe

ratu

re (

F)

CA CZ04

2013

2014

48


FIGURE 31: AVERAGE RELATIVE HUMIDITY DURING THE M&V PERIOD

FIGURE 32: AVERAGE WIND SPEED DURING THE M&V PERIOD

0

10

20

30

40

50

60

70

80

90

Re

lati

ve H

um

idit

y

CA CZ04

2013

2014

0

1

2

3

4

5

6

7

8

9

Win

d S

pe

ed

(M

PH

)

CA CZ04

2013

2014

49


FIGURE 33: AVERAGE CLOUD COVER DURING THE M&V PERIOD

PV SYSTEM

The library has a 40 kWDC rated Photovoltaic (PV) electricity generation array.

As discussed in the PV system description in the “Emerging Technology” section, the original

design phased PV performance calculations were based on an array tilt of 30o to the South,

whereas the final design had the PV array mounted flat on the roof.

Figure 34 summarizes the original design phase PV performance analysis inputs and results.

PV Watts was used for the analysis. The analysis also assumed a 0.770 de-rate factor

(accounting for inverter losses, collector soiling, and other losses), for a total AC power

output of 30.8 kWAC and an annual power generation of 58,032 kWh. Figure 35 shows the

revised PV calculations for the final design’s flat installation. Power capacity remains at 30.8

kWAC , but annual output drops to 50,263 kWh, a 7,769 kWh or 13% decrease. This drop in

expected power led the design team to replace the gas heated HVAC system for the main

reading room with an electric heat pump system. This change reduces site energy due to

the efficiency/COP difference between gas heating and heating with a heat pump.

0%

10%

20%

30%

40%

50%

60%

70%

Clo

ud

Co

ver

CA CZ04

2013

2014

50


FIGURE 34: ORIGINAL PV PERFORMANCE PROJECTIONS FOR 30O

TILT

FIGURE 35: REVISED PV PERFORMANCE PROJECTION FOR PANELS FLAT ON ROOF

Figure 36 shows actual power output measured during the initial system performance

analysis (November-December 2012). The maximum output observed during the initial M&V

was 30.8 kW, occurring on 11/17/12. This matches the projected power output from the PV

Watts design calculations.

51


FIGURE 36: PV OUTPUT (KW) DURING THE INITIAL SYSTEM PERFORMANCE ANALYSIS (NOVEMBER – DECEMBER 2012)

During the initial system performance analysis period (November – December 2012), the

daily incident solar radiation striking the PV panels24 versus the PV output was plotted to

confirm that the PV system was performing as expected. This plot was very similar to Figure

38 (Figure 38 includes additional data through January 2013, which did not result in any

significant change in chart shape or best fit slope. See discussion in next paragraph). The

trend is consistent with expectations and indicates the system is performing well.

Upon commencement of the second M&V period starting in September 2013, significant

periods of missing PV data were identified starting in February 2013. Figure 37 shows an

example of the missing data.

24 The daily incident solar radiation comes from the California Irrigation Management

Information System (CIMIS)’s spatial data (http://wwwcimis.water.ca.gov/SpatialData.aspx),

which reports data at 2km spatial resolution using remotely sensed data from the

Geostationary Operational Environmental Satellites (GOES) coupled with the Heliosat-II

model. Measured insolation data for the site is not available.

0

5

10

15

20

25

30

35

PV

Ou

tpu

t (k

W)

http://wwwcimis.water.ca.gov/SpatialData.aspx

52


FIGURE 37: OCTOBER 2013 PV DATA SHOWING GAPS IN LOGGED DATA (REPRESENTATIVE OF 2/13 – 12/13 PV DATA)

The problem turned out to be a failing communication card in the inverter that sends PV

generation data to the BMS system. Interface Engineering Inc. (the MEP consultant on the

project) worked to get the problem resolved. The card was replaced in mid-December 2013,

and the system has worked since. To fill in missing data, a correlation between daily PV

output versus daily solar radiation25 was developed. This correlation is shown in Figure 38,

and includes data from November 2012 through January 2013. The coefficient of

determination (r2) is 0.92, which along with review of the residuals indicates a very good fit,

and a monthly uncertainty of ±11%. Missing PV data from February 2013 through

December 2013 was estimated using this curve.

25 The daily incident solar radiation comes from the California Irrigation Management

Information System (CIMIS)’s spatial data (http://wwwcimis.water.ca.gov/SpatialData.aspx),

which reports data at 2km spatial resolution using remotely sensed data from the

Geostationary Operational Environmental Satellites (GOES) coupled with the Heliosat-II

model. Measured insolation data for the site is not available.

http://wwwcimis.water.ca.gov/SpatialData.aspx

53


FIGURE 38: INCIDENT SOLAR RADIATION VERSUS PV OUTPUT FOR NOVEMBER 2012 THROUGH JANUARY 2013

Figure 39 compares the PV output design projections for a 30o tilt and 0o tilt to 2013 data

and 2014 partial year data (January – September). The PV system is outperforming the

revised PV Watts design estimate.

FIGURE 39: COMPARISON OF PV OUTPUT DESIGN PROJECTIONS TO 2013 AND 2014 (PARTIAL YEAR) GENERATION DATA

Figure 40 shows 2013 estimated and 2014 measured monthly PV generation data.

It is interesting to note that PV output for the first 9 months of 2014 is almost equal to

2013’s total annual output. PV generation for January, February and September is almost

58,032

50,263 53,939 52,052

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

kWh

54


identical in 2013 and 2014, but summer 2014 PV generation is significantly higher than

2013 generation. The reason for this was explored. All PV output data from the BMS and

missing data estimates were double checked and confirmed. The primary explaining variable

appears to be the fact that cloud cover is higher for 2013 during this period for all months

except April. (refer to Figure 41 below). It is likely that the differences are further explained

by average cloud cover vs. time of day (i.e., cloudier during the middle of the day/afternoon

during peak PV generating hours). Unfortunately, the historical cloud cover data is only

available on an average daily basis. To further assess difference the data, hourly cloud

cover data and ideally solar radiation would be needed. There are also variations in other

weather parameters, such as temperatures, cooling degree days, which could impact PV

generation (i.e., PV output drops with temperature). Further assessment of the differences

between 2013 and 2014 YTD PV output is beyond the scope of this report. However, it

should be noted that the likely issue is caused by the increased cloudiness in 2013, and that

no problems with the PV system are noted; in fact, the PV system is performing better than

expected during both 2013 and 2014.

FIGURE 40: 2013 ESTIMATED AND 2014 MEASURED PV GENERATION

-

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000


kWh 2013

2014

55


FIGURE 41: AVERAGE MONTHLY CLOUD COVER FOR MARCH-AUGUST

TOTAL HVAC ENERGY

Figure 42 compares total measured HVAC electric use between 2013 and 2014. Note that

HVAC energy use is higher for 2013. This corresponds well to the higher heating degree

days in 2013 (refer to Figure 26). Note that measured monthly energy consumption is only

available for total HVAC use, and is not available by specific HVAC unit.

FIGURE 42: 2013 AND 2014 MEASURED HVAC ELECTRICITY CONSUMPTION

INDIRECT EVAPORATIVE COOLER/ HEAT PUMP RTU (EC1)

The main library reading area is served by a single Speakman direct/indirect evaporative

cooler/heat pump RTU (unit EC1). Refer to the “Emerging Technology” section for a detailed

system description.

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Mar Apr May Jun Jul Aug

Ave

rage

Mo

nth

ly C

lou

d C

ove

r

2013

2014

-

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000


kWh 2013

2014

56


During the initial assessment period (September 2012 December 2012), a detailed system

performance assessment was conducted to confirm system performance is as expected.

BMS trend data and temporary data loggers were used to assess sub-system performance.

Based on the data, all systems appeared to be functioning properly: space temperatures

were being maintained, supply air temperatures were modulating as expected, system

scheduling/night setback was working, demand controlled ventilation system was

modulating as expected, space CO2 setpoints were maintained, the fan was operating as

expected, the compressor showed minimal use as expected for the weather, and both the

direct and indirect evaporative cooling systems were engaging as appropriate. Subsystem

performance is discussed in more detail below.

Figure 43 summarizes EC1’s operating the characteristics (i.e., percent of time each system

was in operation) for the initial M&V period of November through December 2012, and all of

2013. During the initial M&V period, the fan operated 31% of the time, the compressor only

operated 6% of the time, the direct evaporator section was active 11% of the time, and the

indirect evaporator section was active 1% of the time. Performance was very similar for all

of 2013, except that there was increased compressor use. This is expected as the initial

M&V period did not capture hotter summer weather.

FIGURE 43: EC1 OPERATING CHARACTERISTICS

Figure 44 shows the supply air temperatures, outside air temperature, incoming (outside)

air temperature, and room temperatures from the BMS system during the initial detailed

system performance analysis in November 2012. The time period includes the Thanksgiving

holiday (Wednesday 11/21/2012 – Sunday 11/25/2012). Room temperatures are

maintained between 65oF and 72oF. The high temperature spikes shown are for the supply

air temperature and occur when AC1 is in heating mode providing hot air to the zones. No

abnormal operations were observed. Note that the anomalous data between 11/14 and

11/15 represents a period when the BMS was not recording data.

31%

6%

11%

1%

34%

15%13%

2%

0%

5%

10%

15%

20%

25%

30%

35%

40%

Fan Compressor Direct Evap Indirect Evap

Pe

rce

nt

of

Tim

e in

Op

era

tio

n

Nov -Dec 2012

2013

57


FIGURE 44: EC1 TEMPERATURE DETAILS FOR NOVEMBER 2012

Figure 45 shows the measured zonal CO2 levels. There are a few cases where CO2 levels

spike around 1000 PPM, but the system appears to be modulating the outside air dampers

to bring levels back down.

FIGURE 45: EC1 SPACE CO2 LEVELS FOR NOVEMBER 2012

50

60

70

80

90

100

110Te

mp

(F)

Supply Air Temp 1 Supply Air Temp 2 West Room Temp Incoming Air East Room Temp

0

200

400

600

800

1000

1200

1400

CO2

Leve

l (PP

M)

East CO2 Sensor

West CO2 Sensor

BMS Off Heating On

BMS Off

58


Figure 46 through Figure 48 plot the logged average hourly electric demand for the HVAC

equipment for the initial M&V period, a typical week, and a typical day, respectively. Note

that units are off (with the exception of CU/FC 4, 5 which serves the electric closet) on

weekends and the Thanksgiving holiday, and during the unoccupied period.

FIGURE 46: AVERAGE HOURLY HVAC KW FOR THE INITIAL M&V PERIOD (OCTOBER THROUGH DECEMBER 2012)

FIGURE 47: AVERAGE HOURLY HVAC KW FOR A TYPICAL HEATING SEASON WEEK (11/26 – 12/2 2012)

0

1

2

3

4

5

6

7

kW

Speakman CU/FC 1/3 CU/FC 2 CU/FC 4,5

0

1

2

3

4

5

6

7

kW


59


FIGURE 48: AVERAGE HOURLY HVAC KW FOR A TYPICAL DAY (FRIDAY 11/30/12)

Figure 49 summarizes the daily HVAC kWh for the initial M&V period. Also shown on this

graph is the daily heating degree days (HDD, base 60). Figure 50 shows the same data, but

with heating degree days plotted against kWh. There is a clear relationship between HDD

and HVAC energy use. The flat line at the bottom shows a constant 10 kWh/day irrespective

of HDD is the energy use during weekends and holidays when the main HVAC systems are

off but the electrical closet heat pump is on.

0

1

2

3

4

5

6

7kW


60


FIGURE 49: DAILY HVAC KWH FOR THE INITIAL M&V PERIOD AND HEATING DEGREE DAYS (HDD) FOR (OCTOBER THROUGH

DECEMBER 2012)

FIGURE 50: DAILY HVAC KWH VS. HEATING DEGREE DAYS

During the second phase of the M&V (January 2013 – September 2014), a number of issues

arose with the reading room RTU. The system had problems meeting morning heating

needs. The unit was taken out of night setback mode in an effort to get the space to

maintain comfortable conditions. It was eventually discovered that the heat pump’s

compressor was being disabled from use (locking out) at relatively moderate outside air

0

10

20

30

40

50

60

70

HVAC kWh HDD

0

10

20

30

40

50

60

70

0 5 10 15 20 25

Dai

ly H

VA

C k

W

Heating Degree Days (HDD)

kWh

(b

lue)

an

d H

DD

(re

d)

Dai

ly H

VA

C k

Wh

61


temperatures. Thus, the heat pump was not providing heating. The M&V team reviewed

trend data and provided this to Sacred Heart Schools and the engineer. Note that during the

time running up to the EC1 replacement, no apparent problems were seen in the BMS trend

data that Cadmus had access to. It appeared that supply air temperatures were modulating

correctly (but this may have been due to the electric duct heaters). Cadmus did not have

access to the airflow data (CFM) that would have been useful to help diagnose this issue.

From the data reviewed, it appeared that there may not have been sufficient airflow. It is

possible that the compressor locking out prevented the fan from ramping up. The unit was

eventually warrantied by the manufacturer. The system appears to be working well now,

and the problem appears to be corrected. The library staff will need to monitor system

performance as it moves into the 2014 heating season.

Aside from the above issues, the primary HVAC unit serving the reading room (EC1)

performance worked well. Key performance parameters for October 2013 are summarized in

the following graphs. Figure 51 shows equipment (fan, compressor, direct evaporative

cooling pump and indirect evaporative cooling pump) status (on/off). Systems are

performing as expected, with no anomalous behavior. Minimal compressor (mechanical

cooling) use is observed, with direct evaporative cooling in operation. Indirect evaporative

cooling is not used, which would not necessarily be expected given the minimal cooling load

during the period and the higher winter relative humidity levels.

FIGURE 51: EC1 HVAC UNIT FUNCTION STATUS (OCTOBER 2013)

Figure 52 shows EC1 CO2 levels and air temperatures for a representative period (October

2013. The y-axis units for CO2 are PPM, and the y-axis units for temperatures are in oF. The

system appears to be under proper control. CO2 spike up during occupancy events, but are

brought back under control as the demand controlled ventilation system increases outside

air ventilation. The highest CO2 levels observed is 1000 PPM (a single event), with the

system responding quickly to bring this down as anticipated.

Unit o

pera

ting s

tatu

s (

hig

h =

on,

low

= o

ff)

62


Only 5 days of heating are observed during the period (heating is indicated by the spike in

the supply air temperatures). This is expected given the daily temperatures during this

period (see Figure 53), as average daily outside air temperatures are dropping during the

last part of the month when heating is observed.

FIGURE 52: EC1 HVAC UNIT CO2 AND TEMPERATURES (OCTOBER 2013)

63


FIGURE 53: DAILY TEMPERATURE RANGES (OCTOBER 2013

PACKAGED HEAT PUMP UNITS

The study rooms and classrooms are served by high efficiency variable speed heat pump

units, summarized under the “Emerging Technology” section.

Figure 54 through Figure 58 summarize the room temperature trend logs for the rooms

served by the packaged heat pumps for November 2012. These temperature trends were

reviewed to identify operational issues, temperature control, and other issues. Room

temperatures indicate that the HVAC systems are generally turning off at night, allowing the

room temperatures to drift during the off periods. It appears that the thermostats all have a

relatively large dead-band (i.e., throttling zone) that allows the space temperature to drift

significantly (anywhere from 2 – 5oF) before the system turns on to heat or cool the room

back to the temperature setpoint. Compare the large daily temperature variations for most

rooms to the much tighter temperature control in the electrical room (Figure 56), where the

unit maintains a ~±2oF setpoint with no night setback. This wide deadband is generally a

good thing from the energy perspective, but it may not be ideal for humidity/moisture

control. It is also possible that the wide swings are indicative of the heat pumps being

undersized to meet zone loads, and/or routine opening of the windows.

The one potential energy conservation measure that Stevens Library may want to consider

is increasing the cooling setpoint for the electrical room, which contains a variety of servers.

The temperature is kept below 69.5oF. Typically, servers have higher acceptable operating

temperature ranges. The server’s temperature specifications should be reviewed and the

cooling temperature setpoint increased accordingly.

0

10

20

30

40

50

60

70

80

90

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Tem

pera

ture

(F)

October 2013

Max

Mean

Min

64


FIGURE 54: ROOM 201 TUTORING ROOM TEMPERATURE (NOVEMBER 2012)

FIGURE 55: ROOM 204 TECH ROOM TEMPERATURE (NOVEMBER 2012)

FIGURE 56: ROOM 206 ELECTRICAL ROOM TEMPERATURE (NOVEMBER 2012)

50

55

60

65

70

75

80

Zon

e T

em

p (

F)

Sample Time

Room 201 Tutoring Room Thanksgiving 11/21-11/23

50

55

60

65

70

75

80

Zon

e T

em

p (

F)

Room 204 Tech RoomThanksgiving 11/21-11/23

50

55

60

65

70

75

80

Zon

e T

em

p (

F)

Room 206 Electrical Room

Thanksgiving 11/21-11/23

65


FIGURE 57: ROOM 207 TEMPERATURE (NOVEMBER 2012)

FIGURE 58: ROOM 211 OFFICE TEMPERATURE (NOVEMBER 2012)

Figure 59 through Figure 63 plot the same temperature data except for the most recent

year-long period from 10/13 through 9/14. Note that these plots contain room temperatures

when the rooms are occupied, as well as unoccupied and in night setback mode. These plots

reveal long term average room temperature fluctuations and indicate HVAC system

performance. Note that maximum and minimum room temperatures fluctuate seasonally

(i.e., when the units turn off in the summer after work, room temperatures drift up towards

warmer ambient temperatures, and vice-versa during winter). This is indicative of proper

night setback control. Temperature setpoints generally appear to be maintained at

reasonable levels.

Figure 59 shows the tutoring room temperatures. Summer average temperatures are

around 70-75oF with night time temperatures in night setback mode drifting up to 80oF.

Winter average occupied temperatures are generally maintained at 68-70oF and allowed to

50

55

60

65

70

75

80

Zon

e T

em

p (

F)

Room 207


50

55

60

65

70

75

80

Zon

e T

em

p (

F)

Room 211 Office


66


drift down to 60oF during unoccupied periods. Note that the tutoring room is a corner room

with more exterior exposure than some of the other rooms (see Figure 4). This exposure

would account for the quicker temperature drops/rises during night setback mode.

FIGURE 59: ROOM 201 TUTORING ROOM TEMPERATURES (10/13 – 11/14)

The Tech Room temperatures (Figure 60) show a similar pattern, but with less extremes.

This is expected given that this room has less exterior exposure than the tutoring room.

FIGURE 60: ROOM 204 TECH ROOM TEMPERATURES (10/13 – 11/14)

Note that the electrical room (Figure 61) temperature is more tightly controlled on the

upper end and is not allowed to go into night setback. This room is equipped with A/C only

to prevent equipment from overheating due to internal equipment heat gains. Due to

internal heat gains, this unit is almost always in heating mode. Note that the thermostat

setting has been adjusted upward from its original low temperature, per M&V feedback. This

will help save energy.

60

65

70

75

80

85

90

Zon

e T

em

p (

F)

Room 201 Tutoring Room

60

65

70

75

80

85

90

Zon

e T

em

p (

F)

Room 204 Tech Rm

67


FIGURE 61: ROOM 206 ELECTRICAL ROOM TEMPERATURES (10/13 – 11/14)

FIGURE 62: ROOM 207 MEETING ROOM TEMPERATURES (10/13 – 11/14)

The office (Figure 63) is interior and temperatures do not drift as far during night setback.

FIGURE 63: ROOM 211 OFFICE TEMPERATURES (10/13 – 11/14)

EXHAUST FANS

The exhaust fan consumes minimal energy; energy consumption for the exhaust fan was

not directly measured due to space constraints in the electrical panel, but was included in

total panel logged data.

60

65

70

75

80

85

90

Zon

e T

em

p (

F)

Room 206 Electrical Room

60

65

70

75

80

85

90

Zon

e T

em

p (

F)

Room 207 Meeting Room

60

65

70

75

80

85

90

Zon

e T

em

p (

F)

Room 211 Office Room

68


LIGHTING

Lighting is provided by a combination of T5 fluorescent tubes and CFL down lights, with

daylight and occupancy control in all main spaces. Lighting power is monitored by the BMS.

Note that the current transducer (CT) installed on the lighting submeter is the wrong size

and reads incorrectly. An updated calibration factor has been developed for this submeter to

correct the BMS reported data. Refer to the “Technology Evaluation” and “Data Validation

and Quality Control” sections for a detailed discussion. Data presented in this section

contains “correct” lighting data. Specifically, data for 10/2012 through 12/2012 comes from

Cadmus’ temporary loggers installed on the lighting circuit, and data for 2013 through 2014

is corrected BMS data. The estimated uncertainty in this corrected lighting data is ±10% for

hourly peak demand (kW) and consumption (kWh) results, and 1% difference between the

corrected BMS data and the Dent data.

For 2013 (for which a complete year of M&V data is available), lighting consumes 5,211

kWh, or 15% of the buildings’ electricity, and the building has a 0.827 kWh/ft2/year annual

lighting power density. For the first nine months of 2014, lighting consumed 5,317 kWh, or

22% of the building total year-to-date. There was a significant increase in lighting electricity

consumption.

Figure 64 plots the monthly lighting electricity use. Note that for 2013, lighting power

dropped significantly during the spring and summer. Summer lighting power reductions are

expected due to summer break. Spring variability is also expected due to daylighting control

and occupancy sensors. There is a noticeable change in 2014 where lighting power stays

comparatively high, does not show the summer break decrease. Extrapolating 2014 data

(assuming a typical monthly electricity use of ~600 kWh), the projected 2014 lighting

electricity use will be ~7,100 kWh for an annual lighting power density of ~ 1.125

kWh/ft2/year. The library should carefully track lighting energy and ensure that lighting

controls (occupancy sensors, daylighting controls) continue to operate correctly, and that

any issues precluding their full utilization (i.e. glare) be addressed.

69


FIGURE 64: MONTHLY LIGHTING ENERGY USE FOR THE ENTIRE M&V PERIOD

Lighting energy consumption was reviewed in detail during the initial M&V period (October –

December 2012) to ensure that the lighting systems and controls were operating as

intended. Figure 65 plots the measured lighting power density (LPD) during November

2012, and Figure 66 shows the detailed load shape for a typical day in November. The LPD

load shape is indicative of occupancy and photo sensor operation, showing that the lights

are dimming and/or shutting off throughout the day as daylighting permits and/or rooms

are left unoccupied. From Figure 65, it can be seen that the peak lighting power density is

just over 2 W/SF on Thursday 11/29/12, with typical daily peak lighting power between 1.5

and 2 W/SF. The average winter weekday LPD is ~0.95 W/SF during occupied hours (7 AM

– 4 PM), and 0.2 W/SF after hours (5 PM – 6 AM weekdays, and most weekends. There is

occasional Saturday or weekend activity (i.e., Saturday 11/17).

0

100

200

300

400

500

600

700

800


kWh 2013

2014

70


FIGURE 65: MEASURED LIGHTING POWER DENSITY (LPD) FOR NOVEMBER 2012

The representative winter weekday LPD profile shown in Figure 65 shows an increase in

lighting power from 10 – 11 PM from approximately 0.2 to 0.5 W/SF (or ~1900 Watts). The

daily load profiles typically show some type of night time lighting increase, but it is usually

smaller and around 0.1 W/SF (630 W). There is also similar night time and early morning

spikes during the weekends occurring one to three times per night/mornings. It is believed

this is site security patrols triggering motion sensor lights.

-

0.5

1.0

1.5

2.0

2.5LP

D (

W/S

F)

Weekend

Weekend

Weekend

Weekend

Thanksgiv

ing H

oliday

Security?

71


FIGURE 66: LIGHTING POWER DENSITY FOR A TYPICAL DAY (MONDAY, 11/12)

DHW

The building is equipped with three tankless electric water heaters, EWH-1 serves the

bathrooms, and EWH-2A and EWH-2B serve the janitor closet. For 2013, DHW consumed 80

kWh, accounting for 0.2% of the building’s total electricity use. Electricity use for 2014 YTD

(through September) is similar.

Figure 67 plots monthly DHW electricity consumption. Two patterns are observed. First,

there is a large spike in hot water electricity use in July or August. This is slightly more than

double typical consumption. This is most likely due to custodial activities preparing for the

school year. The second trend noted is decreased DHW use during June due to summer

vacation.

-

0.5

1.0

1.5

2.0

2.5LP

D (

W/S

F)

Security or

custodial

Lights turn

on full in

morning

when there

is minimal daylighting

Lighting

power

gradually

declines as

daylighting increases Likely

occupancy

sensors in

classrooms and offices

72


FIGURE 67: MONTHLY LIGHTING ENERGY USE FOR THE ENTIRE M&V PERIOD

Figure 68 plots the detailed logged electricity consumption during the initial M&V period. No

significant changes were observed during the second M&V period. There is minimal DHW

use, with many days showing no consumption. Almost all of the consumption is for the

bathrooms, there is nearly no DHW use for the janitor closets.

FIGURE 68: DHW MEASURED ELECTRICITY USE FOR OCTOBER AND NOVEMBER 2012

-

2

4

6

8

10

12

14

16

18

20


kWh 2013

2014

-

0.1

0.2

0.3

0.4

0.5

0.6

25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10

kW

October November

73


DHW consumption was significantly over-estimated during design phase ZNE projections

and for the LEED energy model. The original LEED energy model specified the DHW system

as a tank-type water heater which uses significantly more energy than the tankless heaters

that were specified. Another reason for the over-estimate of water use is the assumption of

the number of hand washes and other hot water draws. LEED water use calculator assumes

daily fixture uses. Based on the consumption data shown in Figure 68, it is apparent that

daily DHW use assumptions are incorrect for this facility.

RAINWATER AND GRAYWATER SYSTEMS

On average, the rainwater and graywater systems use 5.77 kWh/day, or 7.8% of the

Library’s total electricity consumption in 2013. This is a significant portion of the Library’s

total electricity use. Figure 69 shows the monthly rainwater and graywater system

electricity use, both in total kWh/month as well as percent of the Library’s total monthly

electricity use. Figure 70 shows the same consumption data comparing 2013 and 2014.

There is significant seasonal variation, and during summer months the rainwater and

graywater system can represent up to 25% of the total building electricity use. It should be

noted that total building electricity use drops dramatically in summer, so this 25% of the

total needs to be taken in context. The large summer consumption is driven by the

graywater irrigation system. Summer irrigation needs are higher, so the irrigation system is

in operation more frequently. Electricity consumption is very similar between 2013 and

2014.

FIGURE 69: MONTHLY RAINWATER AND GRAYWATER SYSTEM ELECTRICITY USE

0%

5%

10%

15%

20%

25%

30%

-

50

100

150

200

250

300

350

400

450

% o

f to

tal m

on

thly

ele

ctri

city

use

kWh

/mo

nth

kWh/month % of total electricity use

74


FIGURE 70: 2013 AND 2014 COMPARATIVE MONTHLY RAINWATER AND GRAYWATER SYSTEM ELECTRICITY USE

It should be noted that the graywater system treats water from adjoining buildings and uses

this for wider site irrigation. It was considered a “process load” during the initial design

development and not included it in the ZNE calculations. This M&V effort has included the

rainwater and graywater system electricity use in its overall ZNE calculations, since the

equipment is physically located in the library and included on the Library’s total electricity

use submeter. For this project, this does not affect the buildings ZNE status and is not an

issue. However, this could be an issue for other buildings. These types of rainwater and

wastewater recovery systems are increasingly incorporated into green buildings, and

represent a “new” source of building electricity use. Electricity use for these systems is of

interest to the sustainable building community and utility stakeholders. Temporary power

meters were installed on individual pieces of equipment during the initial M&V period of

October-December 2012. A breakdown of electricity use by component is shown in Figure

71. Note that no electricity consumption was measured on the UV filter. Sacred Heart

Schools confirmed that the UV filter is operating. The reason for the zero measured

electricity consumption for the UV filter is unclear. It is possible that the electrical feed

labeled UV filter in the electrical room was mislabeled, or that there was an instrumentation

error (although the logger appeared to be operational). The ozone generator is the largest

single use (39% of the total, although pumping is the largest overall consumer. While the

end use data varies some seasonally, the following rainwater/graywater system end use

distribution is representative of typical end use distributions and indicates the primary

electricity consuming pieces of equipment in the system.

-

50

100

150

200

250

300

350

400

450


kWh 2013

2014

75


FIGURE 71: RAINWATER/GRAYWATER SYSTEM AVERAGE DAILY ELECTRICITY CONSUMPTION (KWH, AND % OF

RAINWATER/GRAYWATER TOTAL)

PLUG LOADS

Plug loads and ceiling fans (both are on the same subpanel) accounted for 5,715 kWh in

2013, or 16% of the total building electricity use. Figure 72 plots monthly plug load energy

use. Consumption is relatively constant except for July through September 2014, where a

noticeable increasing trend is evident. The reason for this is unclear, and the Library has

been notified. While this will not affect the building’s ZNE performance due to the large PV

array, this is a significant increase and should be investigated and corrected if necessary.

FIGURE 72: MONTHLY PLUG LOAD AND CEILING FAN ELECTRICITY USE

Irrigation Graywater Pump, 1.20 , 21%

Rainwater Irrigation Pump, 0.07 , 1%

Graywater Pump for Domestic Uses,

0.05 , 1%

Ozone Generator, 2.24 , 39%

Pump P2, 2.21 , 38%

UV Filter, - , 0%

-

200

400

600

800

1,000

1,200


kWh 2013

2014

76


BUILDING ENERGY MODEL EVALUATION AND CALIBRATION

Building energy modeling is a topic of significant interest in the ZNE community. Energy

modeling is a very powerful tool that has a critical role in optimizing ZNE building efficiency.

Energy modeling is also the primary tool for establishing annual energy budgets for

renewable energy system sizing. In many cases, failure of a building to achieve ZNE can be

traced to back to some type of “failure” in the building energy modeling—i.e., an inaccurate

estimate of actual building energy use which led to insufficient sizing of the onsite energy

system(s), or insufficient margin was provided to account for the normal weather-related

variations, uncertainty in occupant behavior and plug load use, and related factors.

Overestimating building energy use is not as consequential but can lead to oversizing the

onsite energy system and increase project costs. Finally, building energy modeling can be a

useful tool for helping maintain a building’s ZNE status throughout its operations—an

important but often overlooked use of building energy modeling. This section explores a

range of building energy modeling issues with the intent of providing useful information to

help design teams, building owners/operators, and other stakeholders improve the accuracy

and utility of building energy modeling as a tool for achieving and maintaining ZNE

buildings.

CONTEXT

It is useful to note that “building energy modeling” is used and applied in different ways

depending on what one is trying to accomplish. There are a five primary uses for building

energy modeling: (1) optimizing building energy efficiency (ideally used during initial

building design in an integrated design environment), (2) documenting compliance with

energy codes and standards (typically performed near the end of the building design to

“document” as-designed performance per code/standard methodologies), (3) accurately

projecting actual building energy use for sizing ZNE renewable energy systems, for energy

saving performance contracts and related guaranteed savings projects, (4) verifying energy

savings, and (5) supporting a variety of building operational activities such as load

forecasting and automated fault detection and diagnostics. Each use has a unique set of

practitioners, goals, and established approaches to building energy modeling. Traditionally

there has been limited cross-over between each of these different building energy modeling

domains, their practitioners, their targeted building lifecycle phase. Each domain requires a

niche expertise, and involves different stakeholders, customers, team-members and

building phases. The rise of ZNE buildings creates very interesting cross-over opportunities

between the different energy modeling domains. The building energy model now becomes a

critical tool for (1) optimizing building energy performance in the early design phase, (2)

documenting compliance, (3) accurately projecting actual building performance during

operations to size the onsite renewable system and meet ZNE performance requirements

(i.e., for achieving the Living Building Challenge), (4) potentially verifying ZNE performance



maintain ZNE operations.

The five primary building energy modeling applications are discussed in more detail below:

1. Building energy modeling tools were originally developed to help design teams and

researchers improve building energy efficiency. Building energy modeling is a

powerful tool that can help optimize building energy efficiency, particularly if used to

inform and guide the design from the very early stages of design. Effective use of

building energy modeling throughout design is critical to ZNE buildings.

77


Unfortunately, building energy modeling is often reserved for the latter stages of

design to document compliance.

2. The most common use of building energy modeling is to document compliance with

energy codes (i.e., California Title 24 Building Energy Efficiency Standards) and

voluntary sustainability programs such as LEED. There is a well-established industry

of energy modelers and engineers who specialize in compliance energy modeling,

and there are well-established protocols for how to perform this modeling (i.e.,

ASHRAE Standard 90.1 Appendix G). The intent of compliance modeling is not to

predict actual building energy use during operations but to compare the as-designed

building energy use to a hypothetical base-case building that meets code or standard

requirements. Standardized sets of assumptions are used for operating schedules,

occupancy schedules, plug loads, and related variables that are primarily outside of

the design team’s influence (yet can have significant impact on actual building

energy use). Compliance focused building energy modeling is typically used to

inform and guide most ZNE projects. This presents a number of challenges to

achieving ZNE. The root problem is that compliance based energy modeling is not

intended to predict actual building performance. This leads to a variety of issues.

There is typical no feedback to modelers and design teams as to how well the design

phase compliance model predictions match actual building energy use. As long as

code compliance is shown and LEED points are achieved, there are typically no

consequences for energy models that do not accurately predict performance. There is

little incentive for modelers to spend significant time understanding and refining

input assumptions for non-regulated plug loads, occupancy schedules and related

parameters which can significantly impact actual building energy performance. Many

design teams do not utilize building energy modeling to inform and optimize building

design early in the design process, but perform energy modeling on the back-end

after substantial design is completed done to check compliance and determine the

number of LEED points.

3. At the other end of the spectrum, Energy Service Companies (ESCOs) use building

energy modeling to develop energy saving performance contracts, guaranteed

savings projects, and demand side management projects. They have almost none of

the protocol constraints that compliance modelers have and are solely focused on

accurately projecting building26 performance. There are direct financial consequences

for models that do not accurately project performance. This type of modeling, and

the understanding of actual building operations, typical and expected load profiles,

and related “intuition” is important to guide ZNE building sizing. These are skills that

not all compliance energy modelers have, and is an issue that many design teams

may not even be fully aware of.

4. Energy modeling is sometimes used by ESCOs and others to document and verify

energy savings for energy saving performance contracts. This involves detailed

weather corrections, occupancy corrections, and operational adjustments and is

typically guided by the International Performance Monitoring and Verification

Protocol. It should be noted that this is typically a very different modeling process

and effort than that used by ESCOs to develop the energy saving projects on the

front end. This type of modeling, and the skills and expertise to apply a very

different set of protocols would be useful to determine why a building is not meeting

its ZNE targets. Specifically, to identify whether this is weather-related, due to

26 typically existing buildings

78


changes in occupancy or occupant use that could have impacted energy use

intensities from design assumptions, or incorrect design assumptions. Ability to

perform this type of energy modeling in a contentious (potentially litigious)

environment will likely be increasingly important as more ZNE buildings are

constructed and (presumably) problems are encountered attaining actual ZNE

operation.

5. Building energy models are being increasingly used to support ongoing building

energy management efforts, and for load forecasting. The initial calibrated model

described below was used to inform this project’s M&V efforts. This manual process

is labor intensive, and needs to be automated and incorporated into BMS and/or

building dashboard systems to provide real-time and actionable data to inform

facility management personnel how to maintain ZNE operations over the long term.

A very interesting range of automated fault detection, and related monitoring based

(or continuous) commissioning programs are nascent but gaining attention. These

types of systems will be very valuable for ZNE building operators and can use

building energy modeling to inform systems on expected energy use given actual

weather and other parameters.

PURPOSE AND GOALS

The original purpose of this energy modeling evaluation was to calibrate the energy model

at the end of the initial M&V phase (which obtained only a few months of data) and use the

updated model to project whether the Library would attain ZNE over a year period.

Secondary goals of this energy model calibration effort are to understand how well the

original design phase compliance building energy model predicts actual energy use, explore

how simple design phase model tuning efforts (i.e., updating the model to reflect as-built

design conditions) can improve model predictions, explore the effectiveness of using the

energy model to support ongoing M&V efforts to keep the building on track to achieving ZNE

over the long term, and generally explore how to improve the effectiveness of building

energy modeling efforts to support ZNE efforts across the building life cycle.

The specific goals of this building energy model calibration include:

1. Determine how well the original design phase building energy model matches as-

built conditions;

2. Determine how well the original design phase building energy model energy

projections match actual building performance;

3. Document how well calibrating the building energy model to reflect as-built

conditions matches actual performance data when using standard climatic data;

4. Document how well calibrating the building energy model to reflect as-built

conditions and actual schedules, occupancy/use data, HVAC performance and related

information obtained from M&V efforts improves model performance using standard

climatic data;

5. Compare the calibrated building energy model energy consumption projections on an

on-going basis with actual observed system performance to help inform both overall

energy performance analysis and inform investigation of systems performance

anomalies that may be driving deviations from ZNE goals;

6. Document the effectiveness of using the calibrated energy model as a tool for

maintaining ZNE during operations; and

79


7. Document any lessons learned and recommendations to improve building energy

modeling to support ZNE

APPROACH

The following approach is used for the building energy model calibration efforts.

1. First, the design phase building energy model was obtained, reviewed, and its

projections compared to actual data.

2. The design phase building energy model was updated to reflect as-built conditions.

The model was re-run and the energy use projections were compared to the original

design model projections and actual energy consumption.

3. The building energy model was then further calibrated using initial M&V data, the

occupant survey and detailed BMS review to update schedules and other operational

related items. The model was rerun using standard climatic data. Energy use

projections were then compared to initial M&V period energy use. The model

projections were then used to aid ongoing M&V efforts to help identify whether

performance is on track and to identify issues that need further investigation (i.e.,

are there operational problems, equipment problems, or weather variances form

climatic averages).

4. At the end of the M&V period, the model was recalibrated again using the additional

insights from the entire M&V period data.

WEATHER

All energy models used for this project and described below use the California Climate Zone

4 data used in the initial design model. The primary reason for doing so is that we are trying

to compare how the original design phase energy model projections compare to “as-built”

model energy projections to calibrated model projections.

During the initial M&V plan development, the team also considered whether to calibrate the

model using historical weather data for the M&V Period. Calibrating energy models to actual

climatic data can be very valuable. However, obtaining and validating actual historical

weather data for a specific site can be a significant challenge and expense for many

projects. Cadmus investigated different historical weather sources. For this project, the

Atherton weather station does not have all of the sensors required to develop a customized

weather file by the building energy modeling software (eQuest). Furthermore, significant

QA/QC efforts would be required to clean and review the data, fill in missing data, and

format the data into the appropriate file format. Another source of weather data is the US

Department of Energy (DOE). The DOE collects real time weather data and makes this

available on its EnergyPlus.gov website27 for download. This information typically includes

dry bulb temperature, dew point temperature, wind speed/direction, atmospheric pressure,

visibility, cloud conditions, and precipitation type. Unfortunately, the data is provided “as is”

from its various sources and has not undergone quality control review, validation, filling in

missing data and related quality control review. This data typically has gaps that users fill

by extrapolation and time stamp data needs to be reviewed and converted from GMT to

local standard time if needed. This is a significant effort and beyond the scope of most

design team and building operator expertise and budget to do. The most promising source

27 http://apps1.eere.energy.gov/buildings/energyplus/weatherdata_download.cfm

http://apps1.eere.energy.gov/buildings/energyplus/weatherdata_download.cfm

80


for weather data reviewed was from Weather Analytics28, which, for a fee, can provide

historical weather files in standard formats for use in energy models. Their data processing

algorithms include a range of quality control checks and the weather files have been found

to be clean, useable, and accurate on past projects. They provide “synthetic” weather files

for any location using weather forecasting models and all relevant nearby historic weather

sources (e.g., weather stations, weather buoys, etc.) as boundary conditions for the

weather forecast models. Cadmus has reviewed their data files for other projects and found

them to be accurate and suitable for most building energy modeling needs. There are other

companies that provide similar weather data as well29.

Given the challenges and costs most design teams or building managers would incur

obtaining and processing historical weather data, Cadmus decided not to calibrate the

building energy model to historical data, but to rather use a calibration/evaluation process

that has greater potential to be used by design teams and building operators to extend the

usefulness of existing compliance energy models to support ZNE efforts. The calibration

process included correcting the building energy model to reflect as-built conditions; updated

schedules and occupancy data based on an occupant survey; and a detailed review of

system performance with respect to actual performance data, BMS trend logs, and weather

data.

DESIGN BUILDING ENERGY MODEL

The design team used eQuest building energy simulation software to estimate Library

electricity use for LEED documentation and establish annual energy budgets for the PV array

sizing. As noted earlier, the design team initially specified two Coolerado HVAC units with

gas duct furnaces and evaporative cooling to serve the main library reading room. Table 9

compares 2013 actual energy use to the design building energy model. The design building

energy model has the gas heated HVAC units in the main reading room and projects an

annual energy use of 56,439 kwh/year and an annual energy use index (EUI) of 30.6

kBTU/ft2/year. This 30.6 kBTU/ft2 EUI is on the upper end of what the PG&E ZNE Roadmap

Report30 considers “ZNE Capable31”. The 40 kWDC rated PV system was sized to meet this

load, assuming the collectors were installed at a 30o tilt facing south, with a predicted

generation of 58,032 kWh/year. This is just sufficient to make the building ZNE with a 1,593

kWh/year (2.8%) margin. This is a very thin margin given the typical uncertainties in

building energy modeling and factors outside the design team’s control including actual

scheduling and occupancy, plug loads, weather and other factors that could easily drive

energy use higher by more than 2.8% or PV production down by more than 2.8%. In other

words, the original PV size has almost no margin and puts the project at high risk of not

meeting ZNE targets should anything deviate from projections. Note that the design phase

model used the California climate zone 4 weather file.

28 www.weatheranalytics.com 29 i.e., Meteo Group, www.meteogroup.com 30 Pacific Gas & Electric Company, “Road to ZNE: Mapping Pathways to ZNE Buildings in



Report.pdf 31 i.e., the efficiency level at which current ZNE buildings are performing.

http://www.weatheranalytics.com/

http://www.meteogroup.com/

81


FIGURE 73: DESIGN ENERGY MODEL END USE BREAKOUT

TABLE 9: COMPARISON OF 2013 ACTUAL ENERGY USE AND DESIGN ENERGY MODEL

AS-BUILT BUILDING ENERGY MODEL

During design, the PV array was changed from a tilted racked installation to being installed

flat on the roof (primarily for aesthetic considerations). This reduced projected output to

50,263 kWh/year—a 13% reduction and insufficient to meet the original building’s

Cooling, 1,730 , 3%

Heating, 20,929 ,

37%

Ventilation Fans,

2,540 , 5%

Lighting, 10,800 ,

19%

DHW, 9,800 ,

17%

Plug Loads,

10,640 , 19%

2013 Actual Design Model

Cooling 3,290 1,730

Heating 10,351 20,929

Ventilation Fans 7,729 2,540

Lighting 5,211 10,800

DHW 80 9,800

Plug Loads 5,715 10,640

Rainwater/ Graywater 2,750 in plug loads

Total 35,126 56,439

119,850 192,571

19.0 30.6

kBTU/yr

kBTU/SF

kWh

/ Y

ear

82


estimated load using natural gas heating. In order to meet the ZNE design goals and

increase the margin of safety, the Coolerado units with gas heating were replaced with a

similar unit with electric heat pump heating and ancillary electric duct heaters for backup.

The LEED energy model was not updated to reflect this change at the time of M&V startup.

Note that the estimated PV generation of 50,263 kWh/year is lower than actual 2013 PV

generation of 53,939 kWh by3,676 kWh or 7.3%. The updated PV generation estimate is

quite close to actual 2013 generation.

Cadmus updated the original LEED energy model to reflect as-built conditions. Specifically,

the main library reading room HVAC unit was updated from natural gas heating to a heat

pump. The model was reviewed to identify any other significant changes required to align

with the as-built design, but no major changes were identified. Note that at this point the

model was not reviewed in detail to fine-tune specific system details or operational data.

The as-built model update used the California climate zone 4 weather file. The purpose of

this effort was to determine how the design team’s model would predict building total

building energy use once the primary as-built changes were made. The HVAC change

resulted in a 19% reduction in energy use between the original energy model and the

revised energy model. The revised energy model estimates annual electricity consumption

at 45,720 kWh, and an estimated 24.8 kBTU/ft2 EUI. The as-built PV system would

theoretically provide enough electricity to make the library ZNE in this scenario. This is

10,597 kWh, or 30% higher than 2013 actual building energy use of 35,123 kWh.

FIGURE 74: AS-BUILT ENERGY MODEL END USE BREAKOUT

Cooling, 1,730 , 4%

Heating, 10,210 ,

22%

Ventilation Fans,

2,540 , 6%

Lighting, 10,800 ,

24%

DHW, 9,800 , 21%

Plug Loads, 10,640 ,

23%

83


TABLE 10: COMPARISON OF 2013 ACTUAL ENERGY USE TO DESIGN AND AS-BUILT ENERGY MODELS

INITIAL CALIBRATED BUILDING ENERGY MODEL

The as-built energy model was reviewed and calibrated to match operational data obtained

during the initial M&V period. The primary reason for calibrating the model was to estimate

total annual Library energy use to determine if the building would likely meet its annual ZNE

goals, because at the end of the first M&V period, it was not determined whether the M&V

period would be extended for a full year. The initial calibrated energy model predicted that

the Library would meet its ZNE targets, as discussed below. Additionally, once the second

phase M&V contract was approved, the initial calibrated energy model results were used to

compare against ongoing monthly energy use. This proved to be a very useful tool to

determine if ongoing monthly energy use was in line with modeled expectations, with

deviations flagging the need for more detailed investigation to determine what was going on

(e.g., weather differences from climatic averages, equipment or operational issues that

required further review, model calibration issues).

Data for November 2012 was used to calibrate the model because this was the only full

month of typical data obtained in this period32. The following calibration steps were taken:

(1) The model was reviewed in detail to ensure that systems and subsystems were being

correctly modeled and parameters appropriately selected by Cadmus’s energy modeling

staff. (2) Building operation and occupancy schedules were reviewed against data obtained

from the “Occupancy Survey”, and the school schedule (see Appendix C). (3) Individual

system operation and power consumption data were reviewed against BMS trend logs and

data logger data. Note that correct lighting data was used for this model calibration (actual

logged data from the temporary data loggers installed on the lighting subpanel was used).

(4) November 2012 weather was reviewed and found to be very similar to the November

California Climate Zone 4 data, with no significant weather-related changes expected for

November 2012 data. Refer to the discussion below and Table 11 for more information. The

32 October data was available but was atypical due to start up and BMS programming issues,

and the initial M&V period ended in the middle of December.


As-Built

Model

Cooling 3,290 1,730 1,730

Heating 10,351 20,929 10,210

Ventilation Fans 7,729 2,540 2,540

Lighting 5,211 10,800 10,800

DHW 80 9,800 9,800

Plug Loads 5,715 10,640 10,640

Rainwater/ Graywater 2,750 in plug loads in plug loads

Total 35,126 56,439 45,720

119,850 192,571 155,997

19.0 30.6 24.8

kBTU/yr

kBTU/SF

kWh

/ Y

ear

84


initial design phase model calibration used the California Climate Zone 4 weather file, per

the rationale discussed above under “Weather”.

The initial design phase calibration revisions made to the “as-built” model include:

The night cycle controls for the main reading room HVAC unit (EC1) were modified to

reflect observed system performance. The “as-built” model showed significant after-

hour energy consumption for EC1. However, these units are currently disabled after

hours through the BMS system and the metered data showed these units did not

cycle on during the evenings/weekends. Modeled controls were changed to “stay off”

from allowed to cycle on/off after hours.

The fan operating schedules for HVAC equipment were fine tuned to reflect actual

weekday operational hours.

The domestic hot water specifications were updated from electric hot water storage

tanks to electric instantaneous (point-of-use) tankless water heaters. This reduces

storage tank-related standby losses.

Lighting, plug load and domestic hot water Day Schedules were updated to reflect

actual schedules observed from BMS trend logs and metered data. The original

model’s schedules for these end uses did not correlate well to metered data.

November 2012 weather data was reviewed against the California Climate Zone 4

November climatic data used in the model. Summary data is shown in Table 11.

There is a close match between November 2012 and climatic averages, with key

temperature parameters within 5 oF of each other. 2013 heating degree days are

53% higher than the climate file heating degree days. However, the total number of

heating degree days is very small compared to total annual heating degree days.

Due to the generally close match between actual and climate file key variables, it is

expected that November 2012 energy consumption data should match the modeled

November relatively data well, although we do anticipate that the model would show

slightly higher heating energy than actual data based on the HDD data.

TABLE 11: COMPARISON OF ACTUAL ATHERTON TO CACZ04 TEMPERATURES FOR NOVEMBER 2012

CACZ04 Actual Difference % Dif.

Monthly High Temp 78 75 (3) -5%

Average Daily High Temp 66 62 (4) -7%

Average Daily Temp 53 53 0 0%

Average Daily Low Temp 42 47 5 10%

Monthly Low Temp 32 36 4 11%

Average Dew Point Temperature (F) 41 44.8 3.8 8%

Average Relative Humidity 68 77.3 9.3 12%

HDD 185 283 98 53%

CDD 0 5 5 n/a

The initial calibration changes discussed above resulted in the energy model predicting

November building energy to within 8% of November 2012 performance data. Table 12

compares actual November 2012 library energy end use energy consumption to the initial

calibrated energy model results. HVAC energy use is very close, with modeled energy use

slightly lower than actual by 2%. This is expected because November 2012 has more

heating degree days than the climatic data used in the model. Since the HVAC equipment is

85


off during the night when it is coldest outside, we do not anticipate significant increases in

heating energy, and this is reflected in the data. Modeled lighting energy is within 15% of

measured energy. We deemed this to be sufficiently close given expected variations in

occupant schedules, daylighting controls, and occupancy sensor controls that are hard to

capture perfectly in the model (i.e., a 15% variation in actual vs. estimated lighting use is in

line with expected variance between modeled and actual data). DHW energy consumption is

very small, with only 0.5 kWh/month difference (note that the small consumption values

result in a relatively large (33%) variation between modeled and actual energy use.

Modeled plug loads are within 8% of actual data. Note that the design and as-built models

lumped the rainwater/graywater energy use with plug-loads. To be consistent with past

models, the rainwater/graywater energy use is aggregated in the plug loads as well.

TABLE 12: INITIAL CALIBRATED ENERGY MODEL PROJECTIONS COMPARED TO NOVEMBER 2012 M&V DATA

November 2012

Measured Energy (kWh)

November Initial Calibrated Energy

Model Projections (kWh)

Difference Between Measured and Modeled Data

Total Building Consumption 2,082 1870 8%

HVAC 864 850 2%

Lighting 720 610 15%

DHW 2 2.5 33%

Plug Loads 447 410 8%

Overall, Cadmus deemed the initial calibrated model to sufficiently represent actual building

energy use (based on the limited M&V data available) to predict annual energy consumption

for the initial purpose of determining whether the building would meet its design ZNE goals.

Figure 75 shows the initial calibrated model total energy projections for 2013. Table 13

shows the same data in tabular form. The initial calibrated model predicts an annual energy

use of 23,010 kWh and an EUI of 12.5 kBTU/ft2/year. This is lower than the projected

annual PV system generation of 50,263 kWh. Thus, the initial calibrated model predicts that

the Library would meet its ZNE targets based on climatic data, per the original design

intent.

86


FIGURE 75: INITIAL CALIBRATED ENERGY MODEL END USE BREAKOUT FOR 2013

TABLE 13: COMPARISON OF 2013 ACTUAL ENERGY USE TO DESIGN, AS-BUILT AND INITIAL CALIBRATED ENERGY MODELS

Table 13 also compares the initial calibrated model projections to actual 2013 energy use,

the design phase energy model projections, and the as-built energy model projections. Note

that the design phase model’s 620 Therms of natural gas heating energy has been

converted to kWh-equivalent in the table to facilitate easy comparison between model

updates.

The initial calibrated energy model predicts nearly half the energy use of the as-built energy

model. The largest change between the as-built model and the initial calibrated model

comes from DHW. Calibrating the model reduced DHW energy use by 9,770 kWh, nearly a

Cooling, 800 , 4%

Heating, 7,050 , 31%

Ventilation Fans, 2,120

, 9%

Lighting, 7,840 , 34%

DHW, 30 , 0%

Plug Loads, 5,170 , 22%


As-Built

Model

Initial

Calibrated

Model

Cooling 3,290 1,730 1,730 800

Heating 10,351 20,929 10,210 7,050

Ventilation Fans 7,729 2,540 2,540 2,120

Lighting 5,211 10,800 10,800 7,840

DHW 80 9,800 9,800 30

Plug Loads 5,715 10,640 10,640 5,170

Rainwater/ Graywater 2,750 in plug loads in plug loads in plug loads

Total 35,126 56,439 45,720 23,010

119,850 192,571 155,997 78,510

19.0 30.6 24.8 12.5

kBTU/yr

kBTU/SF

kWh

/ Y

ear

Total Energy Use,

23,010 kWh

87


100% reduction in DHW energy use. The DHW correction reduces total energy use by

~21%. The DHW over-estimate in the as-built model has a significant impact on total library

building consumption. The DHW savings are primarily due to significantly less DHW

consumption observed during the initial M&V period compared to the initial design phase

projections. Note that the as-built model also specified tank-type water heaters whereas the

initial calibrated energy model corrected this to tankless water heaters (eliminating standby

losses) per actual design. The next largest calibration savings is from plug loads. In the

design and as-built models, estimated plug loads are 50% higher than plug loads measured

in the initial M&V period. The plug load over-estimate of 5,470 kWh/year represents 12% of

the 2013 measured total building energy consumption. The next most significant change

between the as-built and initial calibrated model is changes to heating and lighting energy,

both of which are over-estimated by ~30% and result in an excess energy projection of

approximately 3,000 kWh, the equivalent of 7% of the total building energy.

The initial calibrated model predicts 35% lower total energy (12,113 kWh) than 2013

measured building energy use of 35,126 kWh. This is expected because 2013 has more

heating degree days and cooling degree days than the California Climate Zone 4 weather

file used by the model. Refer to Figure 26 and Figure 27 for a comparison of 2013 actual

and climate file heating and cooling degree days.

Refer to the following “Final Calibrated Building Energy Model” section for discussion on how

the initial and final calibrated models compare to one another.

During the course of the final M&V period, the monthly library energy use was compared to

the initial calibrated energy model’s monthly projections, as shown in Figure 76. This was a

very useful diagnostic tool to track whether actual energy use was in line with expected

(modeled) data. Consumption that significantly differed from modeled data triggered the

M&V team to review performance and identify causes so they can be rectified in a timely

manner to help keep the library on track to ZNE. One of the immediate observations was

that the initial model was significantly under-estimating winter heating energy consumption.

The higher than projected energy use triggered a deeper review of heating system

performance, to identify potential operational or equipment problems. No apparent

problems were identified and the systems appeared to be functioning as intended. The team

then reviewed the model for potential modeling errors. It was initially believed that the

model was under-estimating heating energy due to the very limited heating data (1 month’s

worth of data in November 2012 which had limited heating system use) used to calibrate

the model with. A deeper review of the climatic data showed that this was exacerbated by

the fact that the actual 2013 and 2014 weather had significantly more heating degree days

than the California Climate Zone 4 data used by the model. Summer and cooling season

energy use matched modeled energy use well. Another issue observed during the final M&V

period was that July, August and September 2014 energy use was significantly higher than

projected energy use, and that July and August 2014 energy use was significantly higher

than in 2013. Comparison of 2013 to 2014 weather showed that July and August 2014 had

significantly more cooling degree days (i.e., hotter) than 2013, which helps account for the

July and August 2013 vs. 2014 discrepancy. Cooling degree days were similar in 2013 and

2014, and August monthly energy use is similar between years. All three months in both

2013 and 2014 had more cooling degree days than the California Climate Zone 4 climatic

data used by the model, which helps explain the difference. A final factor that was found

when exploring the discrepancies is that the plug load energy for July, August and

September 2014 showed a significant rise over past months (see Figure 72). This issue is

flagged to school personnel to investigate possible causes of this increased plug load and

address as necessary. It should also be noted that the rainwater/graywater system energy

88


use turned out to be highly seasonal (refer to Figure 69) and was significantly higher on

average than the November 2012 data used for the initial model calibration.

FIGURE 76: COMPARISON OF MEASURED ENERGY VS. INITIAL CALIBRATED ENERGY MODEL PROJECTIONS

FINAL CALIBRATED BUILDING ENERGY MODEL

The initial calibrated energy model was recalibrated again at the end of the final M&V

period. The same process used to calibrate the initial calibrated model was used, with the

exception that performance data from the entire M&V performance period was used to

inform the calibration. The following calibration steps were taken: (1) The model was

reviewed again in detail to ensure that subsystems were being correctly modeled and

parameters appropriately selected by Cadmus’s energy modeling staff. (2) Building

operation and occupancy schedules were reviewed against data obtained from the

“Occupancy Survey”, the school schedule (see Appendix C), BMS data, and data logger data

and updated as needed. (3) Individual system operation and power consumption data was

reviewed against BMS trend logs, data logger data, and weather data. System level

adjustments were made, as appropriate, to better reflect actual system performance. Note

that the corrected lighting data was used in the final calibration. (4) Actual weather was

compared to California Climate Zone 4 climatic data used by the model to help inform model

calibration and understand where and why actual energy use data would be expected to

vary from modeled data using the climatic average weather file.

The following calibration changes were made for the final calibration:

EC1 RTU (the main system supplying the library reading room):

o The system type for the main reading room AHU was changed from a variable

volume variable temperature system to a single zone system with

supplemental electric heat. This change was needed to more accurately

capture the supplemental duct heater energy use. Note that one of the

constraints and limitations of the building energy modeling software used

-

1,000

2,000

3,000

4,000

5,000

6,000

kWh

Measured Energy Initial Calibrated Model

89


(eQuest) is the ability to precisely model the exact HVAC system performance

characteristics. This is likely to be an ongoing issue as designers continue to

increasingly incorporate advanced and innovative HVAC system that do not

quite fit into standard HVAC system models. Note that this has always been

an issue with energy models, and that energy modelers have often had to use

creative work-arounds to model actual system performance within modeling

constraints. Note that different energy modeling tools have different

capabilities to fine tune HVAC system components, controls, and building

characteristics. A detailed discussion of energy modeling tools is beyond the

scope of this paper, but it is worth noting that generally speaking, energy

models capable of modeling more complex systems and controls (i.e.,

energy+) typically have a higher level of modeling expertise required and

typically have higher modeling costs. There are many tradeoffs in selecting

the most appropriate modeling tool.

o The outside air supply rate was adjusted to match the mechanical schedule.

o The heating COP was updated from 2.7 to 3.9 to match the schedule.

o The cooling efficiency was updated from SEER 15 to SEER 16.5 to match the

schedule.

o The fan power was adjusted from 0.00583 to 0.003 kW/CFM to match the

schedule.

o The nighttime setback was change from “stay off” to “cycle on as needed” to

reflect operational practice. Note that this reverses the initial model

calibration. Upon review of additional data with larger heating and cooling

loads the units were observed to cycle on as needed.

o The heating setpoint and fan schedules were adjusted to better reflect

February and December school breaks.

o The occupied heating setpoint was adjusted from 70oF to 68oF to better align

with the average zonal setpoints obtained from the M&V data.

Rainwater/graywater system

o Rainwater/graywater system energy was included in plug loads in earlier

models. The rainwater/graywater equipment is in a separate mechanical room

outside the occupied zone thermal boundary. These loads were broken out as

process loads outside of the building envelope.

o Note that the initial calibrated model used November 2012

rainwater/graywater electricity use and assumed this was relatively constant

throughout the year. As shown in Figure 69, rainwater/graywater electricity

use turned out to vary significantly throughout the year and the initial

calibrated model significantly under-estimated this load.

Figure 77 shows a breakout of the final calibrated model annual energy end use. The final

calibrated model projects a total annual energy use of 33,265 kWh, and an EUI of 18.0

kBTU/ft2/year. This is very close to 2013’s actual measured EUI of 19 kBTU/ft2/year.

Heating is the largest energy end use, followed by lighting, plug loads, ventilation, the

rainwater/ graywater system, and cooling.

90


FIGURE 77:PLUT CALIBRATED ENERGY MODEL END USE BREAKOUT

Figure 78 compares 2013 and 2014 measured total building energy use against the final

calibrated model projections. The modeled data compares well to actual data, and the

observed variation are as anticipated from reviewing the climatic data. With respect to

heating, there are more heating degree days in 2013 than in and 2014, and the California

Climate Zone 4 file used by the model has the least (refer to Table 7 for HDD data). The

California Climate Zone 4 file has fewer cooling degree days than 2013 and 2014 (refer to

Table 8). We would expect that actual energy would be higher in the summer than modeled

energy. It is generally the opposite. This is explained by the fact that the energy model is

not able to model the evaporative cooling systems well and is not accounting for this energy

savings. There is a significant spike in June and July 2014 cooling degree days. This is

reflected in the 2014 energy use.

Cooling, 1,364 , 4%

Heating, 10,774 ,

33%

Ventilation Fans, 5,158

, 16%

Lighting, 7,403 , 22%

DHW, 82 , 0%

Plug Loads, 5,735 , 17%

Rainwater/ Graywater, 2,750 , 8%

Total Energy Use, 33,265 kWh

91


FIGURE 78: COMPARISON OF 2013 AND 2014 MEASURED BUILDING ENERGY VS. FINAL CALIBRATED ENERGY MODEL

Table 13 compares 2013 actual energy use to the design, as-built, initial and final calibrated

models. The final calibrated model generally tracks all end uses very well. Lighting power in

the final calibrated model is higher than actual data. We believe this is primarily due to the

fact that the model is not able to capture to effectiveness of the occupancy and daylighting

control throughout the library.

0

1,000

2,000

3,000

4,000

5,000

6,000

1 2 3 4 5 6 7 8 9 10 11 12

kWh

Month

Calibrated Model

2013 Actual

2014 Actual

92


TABLE 14: COMPARISON OF 2013 ACTUAL ENERGY USE TO DESIGN, AS-BUILT AND INITIAL CALIBRATED ENERGY MODELS

TABLE 15: COMPARISON OF MEASURED VS. UPDATED ENERGY MODEL TOTAL BUILDING ENERGY CONSUMPTION (KWH)

MONTH FINAL CALIBRATED MODEL 2013 ACTUAL 2014 ACTUAL

Jan 4,395 5,419 4,713

Feb 3,169 5,274 3,703

Mar 3,088 3,115 2,590

Apr 2,389 1,996 1,948

May 2,397 2,244 1,891

Jun 2,099 1,675 1,850

Jul 2,271 1,648 2,530

Aug 2,190 1,800 2,647

Sep 2,180 2,318 2,713

Oct 2,381 2,401 n/a

Nov 2,692 2,774 n/a

Dec 4,014 4,458 n/a

Total 33,265 35,123 n/a

Variance between

measured and modeled annual total

-5% n/a


As-Built

Model

Initial

Calibrated

Model

Final

Calibrated

Model

Cooling 3,290 1,730 1,730 800 1,364

Heating 10,351 20,929 10,210 7,050 10,774

Ventilation Fans 7,729 2,540 2,540 2,120 5,158

Lighting 5,211 10,800 10,800 7,840 7,403

DHW 80 9,800 9,800 30 82

Plug Loads 5,715 10,640 10,640 5,170 5,735

Rainwater/ Graywater 2,750 in plug loads in plug loads in plug loads 2,750

Total 35,126 56,439 45,720 23,010 33,265

119,850 192,571 155,997 78,510 113,500

19.0 30.6 24.8 12.5 18.0

- 61% 30% -34% -5%

6% 70% 37% -31% 0%

Difference Between Final

Calibrated Model

kBTU/yr

kBTU/SF

kWh

/ Y

ear

Difference Between Actual

& Modeled

93


DATA VALIDATION AND QUALITY CONTROL Data validation and quality control (QC) is a critical element of M&V. Cadmus’s experience

on similar projects indicates that building M&V equipment, including non-revenue grade

submeters are typically not subject to the same rigorous commissioning review that other

systems are, and are often incorrectly installed, calibrated, and/or providing inaccurate

information to the owner. The M&V plan outlined a robust set of data validation and quality

control processes which were followed throughout the course of the M&V. A number of

significant issues were discovered and are reported below.

DATA VALIDATION FOR THE TEMPORARY DATALOGGERS

Cadmus’s data validation and quality controls began with the temporary loggers installed for

the M&V process. During the initial site visit, Cadmus metering personnel inspected the

electrical panel locations, access, and confirmed circuit configuration (i.e., voltage, number

of phases) for each circuit to be measured. Cadmus then programmed the data loggers at

the office, selected and cross-checked the appropriate CT size, and tested the CT/meter

configuration for proper operation and data logging at the office. Meters and CTs came pre-

calibrated from the factory, and Cadmus metering personnel performed spot checks to

confirm proper readings. Specially trained Cadmus metering specialists deployed the meters

to the site. Meter operation/logging status was confirmed at deployment. The data loggers

were downloaded approximately two weeks later. Data was reviewed for consistency,

compared against spot check measurements taken at the site and expected readings based

on equipment schedules, and cross-checked against BMS electricity submeter data and

other BMS data to ensure reasonableness of all systems and to spot any issues.

At the onset of the second phase of M&V (September 2013), Cadmus replaced the Dent

data loggers with HOBO dataloggers and cellular communications cards that would allow

ongoing data collection without the need to access the interior of the facility’s electrical

panels on an ongoing basis and providing improved ability to monitor building end uses on a

real time basis. Note that due to physical space constraints in the electrical cabinet, the

temporary logger could not be installed for the lighting subpanel (LCP B-H1)33. Cadmus’

metering team reviewed meter calibrations, proper CT/meter configurations, programming,

logging and communication both prior to installation and at the time of installation. After the

first month’s data collection, the Hobo data was cross-checked against the DENT data logger

data to identify any issues and inconsistencies. No issues were found with the Hobo loggers

and data aligned with data obtained by the DENT loggers.

33 Note that the original DENT loggers were left in place at the end of the first M&V period

(12/2012) with the intent that they would continue to log data in case the contract was

extended. By the time the contract for the 2nd phase was executed and Cadmus downloaded

the loggers (9/2013) some of the data logger batteries had died and data was lost for 2013,

including panel B-H1’s logger. This was not realized until after the new loggers were

installed. Cadmus was unable to install a replacement Hobo logger for B-H1 due to physical

space constraints and the expectation that this panel was already being logged by the BMS,

unaware that the CT had not been corrected. See discussion in following section for details.

94


DATA VALIDATION FOR THE BMS ELECTRIC SUBMETERS (E-MON D-MON

SYSTEM)

During the installation of the temporary data loggers, Cadmus inspected and verified the

installation of the E-Mon D-Mon electric submetering system that provides electric power

and consumption data to the BMS. The E-Mon D-Mon system consists of individual current

transducers (CTs) connected to six submetering units. These in turn communicate data to

the BMS. The CT placement was inspected and is summarized in the following table. The

panel labeling is shown in the following picture.

TABLE 16: E-MON D-MON SYSTEM CT PLACEMENT

E-Mon D-Mon

Meter ID Loads Measured

Panel/ Circuit(s) Measured

Notes

B-L2 Entire Panel B-L2, primarily plug load

circuits and ceiling

fans

Panel BL-2 feed.

B-L1

(1)

Not logging

meaningful data

CT originally

installed on EF-1 but currently disconnected

The E-Mon D-Mon CT for the meter marked "B-L1 (1)"

was installed on the exhaust fan ( EF-1, circuit 9). A 200 amp CT was installed, but the E-Mon D-Mon was configured for a 400 amps CT. This mismatch between expected and actual CT amperage results in meter reading failure. Furthermore, spot measurements of the exhaust fan circuit showed a power of 1 Amp (not expected to vary). This small current is too small for

the 200 Amp CT. Per recommendation from Mark Roark, controls contractor, the CT for this sensor was disconnected to prevent inadvertent mis-use of this data, and the bathroom exhaust fan was added to the

circuits being measured by E-Mon D-Mon meter labeled "B-L1(2)".

B-L1 (2)

Speakman unit, EF-1, split system heat-pumps, east duct heater, panel

B-L2 feed

Panel B-L1 circuits: 1,3,5,7,11,13,15,17,19,21,2

3, 25, 37,39,41

Multiple circuit wires are bundled onto individual CT's by phase.

B-L1

(3)

Instantaneous

Water Heaters

2, 4, 6, 8, 10,

12

Multiple circuit wires are bundled onto individual CT's

by phase.

B-L1 (4)

Rainwater & graywater

equipment

14,16,18,20,24,26,28,29,31

Multiple circuit wires are bundled onto individual CT's by phase.

Panel B-H1

Entire panel B-H1, lighting

panel B-H1 feed

A 200 amp CT is installed but the E-Mon D-Mon meter is configured for a 100 Amp CT. This will cause erroneous readings. The CT needs to be changed to

100 Amps. Temporary data logging equipment is installed on this circuit to cross-check data.

95


FIGURE 79: E-MON D-MON PANEL LABELING

Three issues were identified with the E-Mon D-Mon system during the initial M&V period’s

detailed system performance analysis and reported to the facility and PG&E in the

November 2012 Monthly Report:

96


1. E-Mon D-Mon “B-H1” meter (lighting power loads)

a. A 200 Amp CT is installed but the E-Mon D-Mon meter is configured for a 100

Amp CT. E-Mon was contacted and they confirmed that this will cause

erroneous readings, as the submeter is pre-programmed with the expected

CT’s standard calibration factor. In addition to the CT/meter mismatch, the

200 amp CT is too large for the expected load.

b. Temporary data logging equipment was installed on this circuit to crosscheck

data and confirms that this submeter is off by a factor of 4.8162 (i.e., it reads

nearly 4.8162 times lower than the actual lighting load).

c. Note that this erroneous reading also causes the total building energy to be

off by approximately 13%, since it is calculated by summing the submeter

loads.

d. The CT needs to be changed to a 100 Amp CT. This issue was flagged to the

facility and design team during the initial M&V period in 2012. It was

understood that the CT issue was being corrected. However, the incorrect CT

is still in use and needs to be replaced.

e. A least-squares regression analysis was performed to develop a revised

calibration factor to correct the lighting power loads. Refer to the following

section for details.

2. Panel B-L1 meters (total of 4 meters)

a. Inspection found that the CT for the E-Mon D-Mon meter labeled “B-L1 (1)”

was installed on only the exhaust fan (EF-1, circuit 9). Spot measurements of

the exhaust fan circuit showed a power of 1 Amp (not expected to vary). This

small current is too small for the 200 Amp CT being used. Furthermore, the E-

Mon D-Mon meter is configured for a 400 Amp CT. This mismatch between

expected and actual CT amperage causes an error. The CT must be replaced

with the correct meter.

b. Per recommendation from Mark Roark, the controls contractor, the CT for this

sensor was disconnected and the bathroom exhaust fan was added to the

circuits being measured by E-Mon D-Mon channel 2 to provide meaningful

data.

c. Temporary data loggers have been installed to verify measurements of the E-

Mon D-Mon data.

d. It is recommended that the unused E-Mon D-Mon Panel B-L1 Channel 1 meter

be redeployed once the correct CTs are installed. The most logical

redeployment would be to install this on the Speakman Unit (circuits 1, 3, 5).

3. The BMS calculates total power by adding up the power from the five operating

submeters. However, this reading is inaccurate, because the lighting submeter

(Panel BH-1) has the wrong CT and reads incorrect lighting power. The building BMS

data must be corrected.

97


It was the M&V team’s understanding that the lighting CT had been replaced with the

appropriate CT. However, upon cross-checking the data for the final report, it was found

that CT for the lighting power submeter (panel BH-1) has not been corrected. All lighting

power and total building power data has now been corrected with the updated calibration

factor. Refer to the next section for details. Sacred Heart Schools was notified and they

have replaced the CT with the correct CT.

BMS LIGHTING PANEL (PANEL BH-1) SUBMETER CALIBRATION CORRECTION

As discussed in the previous subsection, there was a CT mismatch on the BMS submeter the

lighting loads (panel BH-1). To make the historical BMS lighting data usable, an updated

calibration factor was developed using the temporary data loggers. To do this, the actual

lighting panel kW obtained from the temporary Dent loggers was plotted against

corresponding data from the BMS lighting submeter, shown in the Figure 80, for the month

of November 2012. The updated calibration factor is 4.8162 (i.e., multiply the BMS’s

lighting power reading by 4.8162 to get the corrected lighting power.

Note that each meter (Dent vs. E-Mon D-Mon system) has a different data recording and

aggregation interval. Prior to plotting, the data from each meter was aggregated on an

hourly basis into average hourly lighting kW. This was the smallest time increment possible

from the data, and aligns with hourly energy modeling and climatic data needs. This

aggregation does lose some of the sub-hourly detail and results in some inaccuracy in peak

demand, but provides accurate consumption (kWh) data. Also note that the CT is oversized

for the load, which introduces additional inaccuracy, particularly for lower power readings.

Estimated uncertainty in this calibration is ±10% for hourly peak demand (kW) and

consumption (kWh) results. Individual data points are relatively noisy, but over a month

period there is 1% difference between the corrected BMS data and the Dent data.

FIGURE 80: UPDATED CALIBRATION FACTOR FOR THE BMS’ LIGHTING PANEL (PANEL BH-1) SUBMETER

Figure 81 shows November 2012 lighting power for the miscalibrated BMS sensor, the

correct reading obtained from the Dent submeter, and the corrected BMS data using the

calibration factor listed above. Figure 82 shows the same data zoomed to 11/26/12 through

y = 4.8162xR² = 0.9371

-00.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

De

nt

(Co

rre

cte

d)

Ligh

tin

g kW

BMS Lighting kW

98


11/30/12 to better illustrate the data. The peak lighting kW data shows excellent

correlation. Note that the slight phase mismatch between peaks is due to small differences

between BMS clock and data logger clock, and the hourly data aggregation. Also note that

the peak data is more accurate than the low after hours lighting power, due to reduced

sensitivity of the CT at this very low end of the CT range.

FIGURE 81: COMPARISON OF ACTUAL (DENT), MIS-CALIBRATED (BMS) AND CORRECTED BMS LIGHTING POWER

MEASUREMENTS (11/1/12 THROUGH 12/4/12)

FIGURE 82: COMPARISON OF ACTUAL (DENT), MIS-CALIBRATED (BMS) AND CORRECTED BMS LIGHTING POWER

MEASUREMENTS (11/26/12 THROUGH 11/30/12)

BMS TOTAL KWH (STEVENS TOTAL KWH USAGE) CORRECTION

As discussed above, the BMS calculates total power by adding up the power from the

five operating submeters. However, this reading is inaccurate, because the lighting

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

11

/1/2

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2 1

1:0

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11

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1:0

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:00

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submeter (Panel BH-1) has the wrong CT and reads incorrect lighting power. The

building BMS data must be corrected.

Figure 83 shows the BMS’s reported total power (blue line; sum of the individual

submeters) and the total power measured by the temporary data logger installed by

the M&V team. Note the significant deviations. Review and analysis indicates that

these are due to the aforementioned lighting CT mismatch.

FIGURE 83: COMPARISON OF MEASURED TOTAL BUILDING POWER VS. BMS REPORTED BUILDING POWER

Figure 84 plots measured building power (from the temporary DENT loggers installed

by the M&V team) and the corrected BMS reported total building power (the incorrect

lighting submeter data from the BMS is replaced with lighting power measured by

temporary Dent loggers installed on lighting panel BH-1, and added to the other BMS

submeter data). The total building energy now correlates very well. Note that the

result is nearly identical to that obtained when using the BMS lighting power

correction factor discussed in the previous subsection.

There are a number of reasons why the data does not align perfectly. One factor is

that the BMS and data logger averaging and recording intervals are different, which

leads to slight differences in reported peak demand. The BMS is reporting hourly

averages, while the Dent submeters recorded 15 minute interval data, which was

post-processed into hourly averages for comparison to the BMS data). Another issue

is that the temporary loggers and the BMS submeters do not all begin recording their

data at exactly the same time. Finally, meter uncertainty results in some error. In

summary, once the lighting correction is made, there is excellent correlation between

total building power obtained from the BMS’s submeters and the temporary Dent

loggers.

-

2.0

4.0

6.0

8.0

10.0

12.0

14.0

kW

Average of Stevens EMON DMON Total Kwh Total Building Dent kW

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FIGURE 84: COMPARISON OF MEASURED TOTAL BUILDING POWER VS. CORRECTED BMS POWER (CORRECTED LIGHTING)

-

2.00

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8.00

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Total Building Dent kW Revised EMON DMON with Dent Lighting kW

kW

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EVALUATIONS The Sacred Heart Schools Stevens Library is a very successful ZNE project. From the

performance perspective, the building is achieving ZNE by a significant margin and is “net

positive” energy in terms of energy production. The building delivers an extremely high level

of efficiency and performance. Aside from concerns about stuffiness, occupants had minimal

concerns about the building. Aside from a few minor issues, the building has operated very

well in its initial two years. The issues that were encountered include failure of the PV

inverter communications card, and a warranty replacement/repair on the main HVAC unit

EC1.

Some very useful data, recommendations and lessons learned have been gained from the

M&V process. These are summarized in the next section and it is hoped these will be

valuable to building design teams, building operators, and others.

It should be noted that this is an easy building type in a favorable climate to achieve ZNE.

The building has a very low space use intensity and low average occupant density compared

to other commercial buildings such as offices. It is operated on an academic schedule with

more breaks (including a summer break with minimal student library use) than most

commercial facilities. It also has very low plug loads, and is in a very mild climate with

minimal heating and cooling needs.

An interesting note on the design side, with potential utility implications, is the fact that the

building likely would not have achieved ZNE (base on a ZNE site energy definition) with gas

heating, based on the available roof area that could be used for PV. The project had to

switch to an electric heat pump system to reduce its site energy use. This brings up an

interesting discussion relating to site zero net energy verses source zero net energy verses

TDV zero net energy. When looking at source zero net energy, the upstream impacts of the

electric grid generation efficiency and transmission and distribution losses must be factored

in. This would require a larger PV system. Also, fuel switching becomes less advantageous.

The benefits of switching from natural gas heating (with an efficiency of 80-95%) to an

electric heat pump (COP ~3) are reduced because the overall grid efficiency must be

accounted for. A detailed evaluation of the broader implications of ZNE definitions is beyond

the scope of this project and treated in other PG&E ZNE activities34, but we do flag this as

an issue that has relevance to PG&E.

RECOMMENDATIONS AND LESSONS LEARNED This M&V project has resulted in a number of recommendations and lessons learned that

may be useful to a variety of stakeholders. The following discussion is organized by topic

(i.e., sub-metering) followed by specific recommendations to key stakeholder groups.

34 Pacific Gas & Electric Company, “Road to ZNE: Mapping Pathways to ZNE Buildings in



Report.pdf



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SUB-METERING

Metering and submetering are critical to ZNE buildings. Without proper, accurate and

sufficient metering it is impossible to track building performance and manage the building to

achieve and maintain ZNE status. This is particularly critical at this facility as the building is

part of a larger master-metered campus and does not have its own utility meter.

Stevens Library is equipped with six electric submeters to monitor and manage electricity

use. There were several metering related issues that this M&V project identified which would

likely have otherwise gone unnoticed. First, two of the current transducers (CTs) in

submetering panel did not match the submeters’ requirements and resulted in incorrect

readings. The CT measuring the lighting loads was a 200 Amp CT, but the submeter was

configured to read a 100 Amp CT. This mismatch resulted in inaccurate readings. The

lighting loads are being recorded by the BMS at 4.86 times lower than actual loads. This

error is compounded because the BMS adds all of the submetered loads to get total building

energy use. The incorrect lighting panel reading resulted in total building energy being

reported as ~20% lower than actual energy use. It is possible that the error could have

gone the other way and reported higher than actual energy use. This has the potential to

jeopardize a building’s ability to demonstrate that is has achieved ZNE. This is problematic

to all parties involved, particularly if an important sustainability program performance rating

is at stake (i.e., loss of LEED energy and atmosphere credit 1 points for energy efficiency;

inability to meet the living building challenge). The M&V team flagged the CT/submeter

mismatch issue on the lighting panel during the initial M&V period and had thought that the

issue had been resolved. During the data review and QC for the final report, unusually low

lighting power densities were observed and investigated, and it was determined that the

lighting panel CT had not been changed (this has since been corrected).

Metering problems have been a common issue for several recent ZNE buildings. Metering is

a specialty field that, depending on the metering equipment involved, can require special

expertise to install and calibrate. It is recommended that all metering and submetering

equipment in ZNE and high performing buildings receive appropriate commissioning and

verification to ensure it is providing accurate data.


Historical BMS trend log data for lighting and total building energy prior to the

12/16/2014 lighting CT replacement are inaccurate. To make the historical

data useful, apply the lighting calibration multiplier. Note: this calibration

multiplier has been applied to the BMS data presented in this report.

One submeter is currently unused. It would be valuable to operations to

connect this and pick up power or current data for EC1 and the duct heaters.

The submeter readings for the other new buildings should be spot checked

with a hand-help multimeter to confirm their accuracy.


Metering and submetering is often left out of commissioning scopes and

metering problems often go unnoticed. The design team should ensure that

the commissioning scope includes commissioning for submeters. This is

particularly critical for ZNE buildings and other high performing buildings

where having accurate data is vital to achieving and maintaining performance

goals. It is not inconceivable to envision a situation where a building would

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“fail” to achieve ZNE or properly document performance due to a simple

metering issue and miss out on LEED points or miss out on achieving a rating

(i.e., the Living Building Challenge).

The design team on this project did a great job designing the electrical

circuits to facilitate easy submetering. However, many building electrical

layouts present significant challenges and preclude easy and inexpensive

submetering. Design teams should make sure to include submetering

requirements in the project and ensure that appropriate design team

personnel are aware of these requirements.


Metering and submetering issues are a common theme observed on multiple

projects. There are opportunities to encourage projects to improve the

submetering and metering process and make this data more useable and

useful for building owners to achieve and maintain ZNE or similar energy

performance goals.

BMS TREND DATA

This project had excellent BMS trend data to work with. This was invaluable to the M&V

efforts and will be extremely useful for the school to help manage and maintain its ZNE

status. Part of the success was due to the fact that the M&V consultant was able to

coordinate with the controls contractor before the BMS programming was finished, and it

was easy to set up the desired trend logs. Unfortunately, this is not always the case on

projects. More often than not the rich BMS data vital to managing ZNE and deep energy

efficiency is very difficult for building operators, M&V personnel, and others to access, and

therefore it is not used to the extent it could be.

Another issue encountered in the BMS trend data is that the PV power and energy points

were configured to log data on a change in value, rather than a fixed time increment. This

resulted in a massive amount of data (tens of thousands of records per year) that is very

difficult to utilize. Each time increment is different, so it is difficult to overlay this data with

other data (i.e., building performance data). It is possible that the very heavy

communication load placed on the inverter’s communication module to report data on such

a frequent basis may have contributed to the problems it experienced.


Reviewing the BMS trend logs on a monthly basis through the M&V program

has been very useful in identifying issues and spotting problems early and is

critical to maintaining long term ZNE status.

Explore opportunities to automate the routine monthly BMS trend log

downloads and include key performance indicators on the building dashboard.

Examples include plotting monthly energy use against calibrated modeled

energy use. Any significant deviation from monthly expectations could help to

identify and respond to significant issues early.

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It would be valuable to develop a coordinated M&V approach that outlines key

BMS data to trend, time increment for trending the data, defining which

circuits need to be submetered, and give thought to how various stakeholders

charged with meeting ZNE performance goals will be able to access the data

in a quick and easy way. Leaving these as ad-hoc decisions that the controls

contractor has to make on the fly is not optimal for leveraging the usefulness

of the BMS data for meeting ZNE goals.

Downloading and processing BMS trend data remains a complex and time

consuming job for building O&M personnel. The design team and controls

contractor should jointly consider opportunities in specifications and control

system selection that would help facilitate ready use of appropriate BMS data

vital to managing and attaining ZNE and related performance goals.


Design teams are becoming increasingly accountable for building performance

and will have an increased stake in how well buildings perform. There are

numerous opportunities for the design team to enhance the effectiveness of

monitoring, controls, and related systems through thoughtful design. There

are significant opportunities to further explore and promote these

opportunities from the utility and policy side.

AUTOMATED FAULT DETECTION AND DIAGNOSTICS

The BMS system produces a large amount of very useful data that are being stored in trend

logs. Unfortunately, this data is not always easily accessible to typical building owners and

operators. The data must be manually downloaded to a spreadsheet and processed, which is

a time consuming process. Typically, this data is rarely reviewed and therefore provides

little actionable information to inform building owners/operators on an ongoing basis.

Making better use of this data will be crucial for ensuring ongoing ZNE performance by the

Library and other ZNE buildings.

An emerging set of complementary automated “Fault Detection and Diagnostic” (FDD)

software tools and related building dashboard tools are coming onto the market which will

facilitate use of this detailed BMS data and automate much of the labor-intensive review and

processing. While the building automation system is capable of controlling equipment, data

display, alarming and trending, it is not capable of detailed fault detection and

troubleshooting. Fault detection and diagnostics software is capable of conducting custom

detailed analysis on the data handled by the building automation system and serving it in a

graphical method that is intuitive to the user. The appropriate fault detection system, much

like the automation system, is flexible enough to be modified and updated to accommodate

future changes to systems and sequences of operation. This software package gives the

user the capability to run analytics across the entire range of control points within the

automation system, generate and distribute alarms, display data graphically and make

corrections to setpoints and schedules accordingly.

Ongoing commissioning of the building systems is the primary intent of the automated fault

detection system. While commissioning and re-commissioning of systems is effective for

instantaneous verification of correct system operation, fault detection systems continue to

watch building systems long after start up and initial testing is complete. The combination of

ongoing monitoring and custom analytics provides a platform for continued system

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optimization and a real-time view of the buildings energy consumption. One example of a

FDD system is SkySpark, illustrated below.

FIGURE 85: EXAMPLE OF SKYSPARK FDD SOFTWARE INTERFACE


Explore opportunities to expand the Lucid Designs dashboard to include some

of the automated diagnostics and fault detection reporting that will help the

Steven’s library maintain ZNE status and minimize facilities impact for

downloading and processing BMS data. This could include things such as

comparing monthly building EUI to predicted EUI (from calibrated model) and

reporting significant deviations.


Design teams will want to watch this nascent field carefully. There are some

very exciting developments that may be useful to incorporate into high

performing building projects to help ensure challenging performance targets

are met.

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AFDD is an exciting development in the building industry and has significant

potential to help improve long term building performance. Emerging

technology studies and similar efforts to document performance impacts and

best practices with AFDD systems would be very valuable.

ENERGY MODELING

Building energy modeling is used and applied in different ways depending on what one is

trying to accomplish. Each use has a unique set of practitioners, goals, and established

approaches to building energy modeling. Traditionally there has been limited cross-over

between each of these different building energy modeling domains, their practitioners, their

targeted building lifecycle phase. Each domain requires a niche expertise, and involves

different stakeholders, customers, team-members and building phases. The rise of ZNE

buildings creates very interesting cross-over opportunities between the different energy

modeling domains. The building energy model now becomes a critical tool for (1) optimizing

building energy performance in the early design phase, (2) documenting compliance, (3)

accurately projecting actual building performance during operations to size the onsite

renewable system and meet ZNE performance requirements, (4) verifying ZNE performance



maintain ZNE operations. There is need for increased education about the different ways

energy modeling can and needs to be applied to ZNE buildings.


ZNE building projects will require a higher level of modeling accuracy and

applying energy modeling for different purposes. Increased design team use

of building energy modeling is required and team energy modeling expertise

must generally increase as well..

Design teams need to understand that there are different uses for energy

modeling throughout the project life cycle, and effectively use energy

modeling at each phased.

Design teams should be very careful to understand the difference between a

“compliance energy model” and an energy model used to estimate actual

building operational energy for ZNE renewable energy system sizing.

A final “as built” energy model should be developed and used to confirm ZNE

estimates.

Appropriate safety margins should be built into ZNE renewable energy system

sizing to account for weather, occupancy, schedule, space use intensity, and

plug load variance that are likely to occur.

Standard assumptions for plug loads, DHW, and other loads which do not

typically matter as much in compliance modeling (since they are assumed

equal in both the design and base-case and do not typically appreciably

impact compliance energy savings projections) should be very carefully

evaluated. These loads are often significantly different from actual building

loads and poor estimates can jeopardize a building’s ability to achieve ZNE.

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There is a significant need to develop modeling guidelines and best practices

for practitioners to transition from “compliance” modeling to “performance”

modeling. The National Renewable Energy Laboratory’s Building America

Program, for example, developed a set of energy modeling guidelines and

data for residential energy modeling that were very useful to practitioners.

Similar approaches could be taken for commercial building modeling. Existing

databases (i.e., CUESS) could be leverage to help develop guidelines for

water heating energy use and other relevant loads. Water heating energy use

was significantly over-estimated for this project.

PLUG LOADS

Plug loads comprise an increasingly large percentage of the total building energy use as

HVAC and other regulated loads are reduced. It is not uncommon for plug loads to represent

25% - 50% of a ZNE building’s total load. The Library’s plug loads are relatively small

compared to typical buildings, accounting for 16% of the total building energy use in 2013.

The original energy model over-estimated plug load energy by ~50%.

Note that a significant upward trend in plug load energy began in July 2014 and continued

through the end of the M&V period in September 2014. Refer to Figure 72. The reasons for

this increased consumption is unclear, but could include significant new equipment

additions, equipment not being turned off, some type of equipment malfunction, the use of

a portable electric heater(s), or similar issues.. The reasons for this should be investigated

and corrected if needed by Library staff.

The key lessons learned are that plug loads represent a large portion of building energy use

and focusing on opportunities to reduce these loads will be important for future ZNE

buildings. Furthermore, it is important to refine energy modeling efforts to estimate these

as accurately as possible. There may be opportunities for PG&E and other organizations to

support projects to improve the modeling of plug loads. As a starting point, it would be

useful to document how well plug loads are currently being modeled (e.g., a study

comparing LEED building energy modeled data vs. actual plug loads).

ELECTRICAL ROOM TEMPERATURE SETPOINT The electrical room, which contains a number of servers, was initially maintained between

66 oF and 69.5oF. Typically, servers have higher permissible operating temperature ranges

(server temperature specifications should be reviewed to determine thresholds), and typical

temperature setpoint relaxed accordingly to reduce air-conditioning energy use. Sacred

Heart increased its temperature setpoints in the electrical room to 74-76oF.


Explore opportunities to specify equipment with robust temperature operating

ranges, and make sure this information is communicated to building owners,

control contractors who set initial temperature setpoints, commissioning

agents, and other building operations stakeholders.

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LIGHTING

The Library uses linear fluorescent lighting with daylighting and occupancy controls to

reduce peak lighting power density (LPD). LED lighting is becoming increasingly cost

effective and can be merged with advanced control strategies, individually controlled

luminaires, and advanced control strategies to minimize lighting energy.


Design teams should specify LED lighting and advanced control strategies that

are well matched to LED lighting technology.


Programs such as the Emerging Technologies Program provide invaluable

information to the design community on what works and what does not,

costs, and other barriers and opportunities related to the installation and

performance of emerging products and technologies. There is an ongoing

need for this information regarding emerging lighting technologies and

practices (i.e., “occupant specific lighting”).

ONSITE WATER RECYCLING AND RAINWATER CAPTURE

Nearly 10% of the library’s energy is spent on the rainwater and graywater systems.


No electricity use was measured on the graywater system’s UV system. The

facility should check to ensure the UV system is operating correctly.


Consider the energy impacts of onsite water systems. Make sure to include

these loads in the relevant ZNE and PV array sizing calculations if they are to

be included in the “ZNE” load. Specify efficient and appropriate equipment

and systems and ensure they are performing as expected.


Water/energy/carbon nexus issues are increasingly becoming a part of

building-level design. This is an area where designers could use guidance on

best practices.

VENTILATION AND AIR QUALITY The operational survey indicated there is a tendency for the building to feel hot or stuffy

during high occupancy periods or hot weather. Occupants use windows (natural ventilation)

for supplemental ventilation. There are a number of potential reasons for this condition,

which are beyond the scope of this M&V effort to fully investigate. The M&V efforts did note

that there is limited compressor use for the main reading room, and it is possible that this

system is not providing adequate humidity control. Room humidity is not one of the trend

logs available on the BMS. It is possible that fine-tuning of the controls could address this

109


(e.g., CO2 level setpoints, evaporative cooling staging, supply air humidity control

setpoints), or it could be that the building is operated per its design intent, and that some

occupant education on the building design and use of the natural ventilation and ceiling fan

features to provide additional airflow could address the issues.


Investigate humidity levels if building occupants continue to note hot and

stuffy conditions. Fine-tuning of minimum ventilation rates and demand

controlled ventilation controls sequences may be required.

CEILING FANS The operational survey indicates that the ceiling fans are noisy and are not used often.


Issues such as noise have a demonstrated impact on occupants use of the

ceiling fans and other equipment. Designers should carefully consider noise

and related issues which may impact user acceptance and use of equipment

and strategies.

In summary, Stevens Library is performing very well and meeting its ZNE goals. The most

important recommendations to the facility is to make sure that the incorrectly sized CT’s on

the BMS electricity submeters are replaced with the correct sized CTs, or have the updated

calibration factor programmed into the BMS. We also strongly recommend that Sacred Heart

Schools continue some type of M&V for not just the library, but all of its buildings to ensure

efficient and cost effective operations. For the design team, the most significant

recommendations for future projects would be to continue refining the energy modeling

process. Plug load, rainwater/graywater system, and DHW heating energy projections were

significantly off. This does not significantly impact this building, but these mis-estimates

could significantly impact ZNE attainment for another building type. Also, the design team

did an excellent job designing the electrical system to be well metered, and included a front

end dashboard. At this point however, it will most likely take strong design team leadership

to ensure that the data logging capabilities are translated into useful and actionable data on

the dashboard that will help building managers maintain long-term ZNE performance. From

the utility perspective, there are significant opportunities more effectively incorporate

submetering into buildings and work with controls contractors, building dashboard

developers, and building operators to make this data useful and actionable. Automated fault

detection and diagnostics will play an important role in managing the massive amounts of

data that submeters and BMS systems generate.

110


APPENDIX A: ADDITIONAL ELECTRICAL AND

MECHANICAL SYSTEM DETAILS System details, drawings, schedules, control sequences, BMS points, and related

information are excerpted from the drawings and other design documents below for

reference and future diagnostic efforts.

ELECTRICAL SYSTEM (LAYOUT AND PANEL CONFIGURATION) The following figure shows the 1-line electrical diagram for the library. All of the buildings

are fed from a single main distribution panel. The library’s PV system also feeds directly into

the main switchgear via a separate distribution panel, which is separate from the building

feed. There are no interconnections between the PV system and building supply panels in

the library’s electrical room.

111


FIGURE 86: BUILDING B ELECTRICAL 1-LINE DRAWING

(Excerpted from Drawing E601B showing library distribution, with other building feeds

removed for clarity)

112


The following schedules show the individual circuits serving the library. All of these panels

are located in the library’s electrical room. The following schedules are excerpted from

Drawing E701B. The library is served only by electricity. The first schedule summarizes the

mechanical equipment electrical connections and is not an actual panel schedule. Panel

schedules follow.

FIGURE 87: MECHANICAL EQUIPMENT ELECTRICAL CONNECTION SUMMARY

FIGURE 88: DISTRIBUTION SWITCHBOARD DP-BH SCHEDULE

113


FIGURE 89: PANEL B-H1 SCHEDULE

FIGURE 90: PANEL LCP B-H1 SCHEDULE

114


FIGURE 91: PANEL B-L1 SCHEDULE (AS-BUILT)

Note: Figure 91 shows the as-built drawing for electrical panel B-L1. The markups are made

by the contractors and show the corrections as installed.

115


FIGURE 92: PANEL B-L2 SCHEDULE (AS-BUILT)

CONTROLS/BUILDING MANAGEMENT SYSTEM (BMS) The following figure summarizes the BMS layout. Each building has a local controller

connected to a central system.

116


FIGURE 93: BMS 1-LINE DIAGRAM

117


APPENDIX B: OPERATIONAL SURVEY The purpose of the operational survey is to obtain space usage data to correlate with M&V

data, to identify any related issues that affect performance or comfort (e.g., temperature

control problems), and to aid in model calibration.

The following survey questions were answered via dialogue with the librarian.

OPERATING HOURS What are the library’s typical operational/open hours?

Library is typically occupied/open between 7am-4:30pm; weekdays.

o Monday: 7:30-3:30pm

o Tue-Thur: 7:30 – 4pm

o Friday: 7:30-3:30pm

Some weekend occupancy occurs utilizing the space for meetings and other

activities.

What time do the librarians typically arrive and depart?

7am-4:30pm; Weekdays

Can you please describe the typical occupancy/use patterns for the library

throughout the day? Does this vary throughout the week?

Typical (average) number of people in the main library reading room throughout

the day:

Best guess 75-100 people per day.

Librarian has ~ 14 classes/week; 18 students/class. 15-30 minutes per class.

Tutoring occurs in the main library.

Some students use the facility during lunch (12pm-1pm).

Maximum number of people in the main library likely to be encountered

During standardized testing, there were approximately 50 students occupying the main

library (isolated case).

Other than this event, max number is estimated at around 30 people.

Describe the typical occupancy/use for each of the classrooms, tutoring rooms

and other rooms receive?

118


Tutoring Room (Rm 203): 2 people

Conference Room: When occupied, typically 2-4 people.

Tech Office: 2 employees, students visit throughout the day for computer servicing.

Office Next to Tech Office: 2 employees

Are there significant variations in occupancy throughout the day or week

(describe)

Yes. Students come to the tech office for computer support. This occurs

throughout the day, heavy traffic occurs during lunch.

Does the library receive significant weekend use?

No. Once/month.

When and how often does the custodial staff clean the library?

One entry/exit per day.

TEMPERATURES What best describes the temperature conditions of the main reading room:

o Typically “just right”

o Generally “just right”, but occasional temperature swings or other issues.

If yes, describe:

During the winter, the mornings are cold, but generally warm up throughout the

day.

Temperature is never too hot. Climate is described as cool to comfortable.

Can you identify the cause, or when the temperature swings typically

occur? (e.g., mornings, when it is hot/cold out, when the sun shines

through the window, when the space is at capacity, etc.)

o Too cold

Previously discussed with the librarian that a few weeks back, the Speakman unit

was not providing sufficient heat. This has been since corrected.

o Too hot:

In the summers when the sun is out, feels the space can be a bit warm.

o Significant temperature fluctuations/variations

If yes, describe:_________________




When there are a number of people occupying the library at a given time (i.e.

standardized testing), interviewees feel that there is insufficient outside air and

the HVAC system has trouble maintaining space temperature.

119


What best describes the temperature conditions of the smaller classrooms and

tutoring rooms:

o Typically “just right”

o Generally “just right”, but occasional temperature swings or other issues.

If yes, describe:

Occupant generally is content with the exception of that they feel it gets “stuffy”.

Typically opens windows.

Tech office gets a bit warm due to the number of electronic equipment in the

space. Staff doesn’t complain, and opens windows the room feels uncomfortable.




o Too cold

o Too hot

o Significant temperature fluctuations/variations

If yes, describe:_________________

Tech office gets a bit warm due to the number of electronic equipment in the

space. Staff doesn’t complain, and opens windows when the room feels

uncomfortable.




Are the spaces able to maintain temperature setpoints during large occupancy

changes (e.g., a class entering/leaving a room, or swings between full and

minimal occupancy?)

Interviewee thinks so.

Are the night-time temperature setback times set appropriately?

Unaware of temperature set-back controls.

Does the space warm up in time for the normal occupancy hours?

Chilly in the mornings. Typically warms up around 8:20 am.

VENTILATION AND FRESH AIR Are you satisfied with the ventilation and fresh air?

Consistent remarks regarding air quality. Occupants feel that private offices and

libraries can get stuffy. Occupants of private spaces typically leave windows

open.

Please describe any problems or issues related to ventilation (e.g., the classrooms

get stuffy when a class is in there, excessive noise, etc.)

120


When it is warm outside and/or there are a number of people in the building,

occupants feel uncomfortable. Consistent remarks regarding air quality.

CEILING FANS Are the ceiling fans used? If so, how often?

Ceiling fans are manually operated. Librarian only uses these fans when the

library is fully occupied. Feels the fans create a lot of noise.

LIGHTING Have you noticed whether the lights dim in response to daylighting?

Unaware of dimming controls on the lighting system.

Have you noticed excessive glare, or solar heating?

More so in the summer, there is constant glare and it warms up the reading

room. Librarian typically utilizes the window shades during this time.

Are there any other issues related to the daylighting systems?

None. Not aware that the site had daylighting controls.

Are the occupancy sensors working correctly?

Thinks so. But staff noted sometimes people forget to turn lights off when they

leave. I don’t think they are aware they have motion sensors.

Have you noticed whether the ceiling fan operation keeps the occupancy sensor

engaged?

No.

Are there any other problems or issues related to lighting?

None.

121


EQUIPMENT AND PLUG LOADS

TABLE 17: LIBRARY EQUIPMENT AND PLUG LOADS COUNT

Equipment Quantity Notes/Comments desktop computers and monitors 10 Laptops TV’s and additional

screens/monitors 1 In reading Room

Printers 3 large photocopiers - small/medium photocopiers - refrigerators/freezers -

Any other significant equipment or

appliances? (Please list below) Computer cart/charging

station in tech room for

laptops.

OTHER ISSUES What is your overall impression of the library?

It’s ok.

What do you like best about the new space?

No comments

Are there any areas that need adjustment or fine-tuning?

Bathroom stinks.

Are there any other operational issues that need to be addressed?

Needs to address air quality.

122


APPENDIX C: SCHOOL SCHEDULE The Academic Calendar is available online at http://www.shschools.org/page.cfm?p=1465 .

Holidays and other key events are summarized below

FALL 2012

Sunday - August 26, 2012 Mass and Grand Opening

Celebration 1:00 PM

Monday - August 27, 2012 SHP Student Orientation/First

Day of Classes Prep Campus

Tuesday - August 28, 2012 Grades 1-8 HALF DAY Lower and

Middle School Campus

Wednesday - August 29, 2012 Grades 1-8 Full Day 7:50 AM to

2:25 PM Lower and Middle School Campus

Thursday - August 30, 2012 Grades 1-8 Back to School Night

6:00 PM to 9:00 PM

Monday - September 3, 2012 LABOR DAY - ALL SCHOOL NO

CLASSES

Monday - October 15, 2012 FALL BREAK - ALL SCHOOL NO

CLASSES

Tuesday - October 16, 2012 Prep Parent Conferences - SHP

NO CLASSES 8:00 AM to 7:00 PM

Wednesday - October 31, 2012 1st-8th Half Day 11:40 AM Lower

and Middle Schools Campus Dismissal at 11:40am (1st-8th) 1st-8th Parent Conferences 1:00

PM to 4:00 PM Lower and Middle Schools Campus

Thursday - November 1, 2012 Grades 1-8 & Montessori Parent

Conferences - NO CLASSES 8:00 AM to 6:00 PM Montessori & Lower/Middle School Campuses

Friday - November 2, 2012 - NO CLASSES Lower & Middle

School Campus

Wednesday - November 21, 2012 Thanksgiving Break - ALL SCHOOL

NO CLASSES

Thursday - November 22, 2012 Thanksgiving Break - ALL SCHOOL

NO CLASSES

Friday - November 23, 2012 Thanksgiving Break - ALL SCHOOL

NO CLASSES

Monday - November 26, 2012 ALL SCHOOL - CLASSES RESUME

Friday - December 21, 2012

Grades 1-8 & Montessori Dismissal for Christmas Break - HALF DAY Lower/Middle School

Monday - December 24, 2012 Christmas Break - All School NO

CLASSES

Tuesday - December 25, 2012 Christmas Break - All School NO

CLASSES

Wednesday - December 26, 2012 Christmas Break - All School NO

CLASSES

Thursday - December 27, 2012 Christmas Break - All School NO

CLASSES

Friday - December 28, 2012 Christmas Break - All School NO

CLASSES

Monday - December 31, 2012 Christmas Break - All School NO

CLASSES

Tuesday - January 1, 2013 Christmas Break - All School NO

CLASSES

Wednesday - January 2, 2013 Christmas Break - All School NO

CLASSES

Thursday - January 3, 2013 Christmas Break - All School NO

CLASSES

Friday - January 4, 2013 Christmas Break - All School NO

CLASSES

Monday - January 7, 2013 Christmas Break - All School NO

CLASSES Faculty/Staff Retreat - All School

NO CLASSES

SPRING 2013

Tuesday - January 8, 2013 All School - CLASSES RESUME

Monday - January 21, 2013 Martin Luther King Jr. Holiday -

All School NO CLASSES

Monday - February 18, 2013 Winter Break - All School NO

CLASSES

Tuesday - February 19, 2013 Winter Break - All School NO

CLASSES

Wednesday - February 20, 2013 Winter Break - All School NO

CLASSES

Thursday - February 21, 2013 Winter Break - All School NO

CLASSES

Friday - February 22, 2013 Winter Break - All School NO

CLASSES

Monday - February 25, 2013 All School -CLASSES RESUME

Thursday - March 28, 2013 Easter Break - All School NO

CLASSES

Friday - March 29, 2013 Easter Break - All School NO

CLASSES

Monday - April 1, 2013 Easter Break - All School NO

CLASSES

Tuesday - April 2, 2013 Easter Break - All School NO

CLASSES

Wednesday - April 3, 2013 Easter Break - All School NO

CLASSES

Thursday - April 4, 2013 Easter Break - All School NO

CLASSES

Friday - April 5, 2013 Easter Break - All School NO

CLASSES

Monday - April 8, 2013 All School -CLASSES RESUME

http://www.shschools.org/page.cfm?p=1465

123


Friday - May 17, 2013 Grades 1-8 In-Service Day

Friday - May 24, 2013 Prep Graduation 5:00 PM Soccer

Field

Monday - May 27, 2013 Memorial Day Holiday - All School

NO CLASSES

Friday - May 31, 2013 Prep Grades 9-11 End of

Semester Exams

Friday - June 7, 2013 Dismissal for Summer Break

FALL 2013 Sunday - August 25, 2013 Freshman Orientation SHP

Campus

Monday - August 26, 2013 Prep Full Day SHP Campus

Tuesday - August 27, 2013 Lower School Half Day 7:50 AM

to 11:40 AM Bergeron Lower School

Middle School Full Day 7:50 AM to 3:15 PM Xie Middle School7:50am-3:15pm

Wednesday - August 28, 2013 Lower & Middle School Full Day

7:50 AM to 2:25 PM Bergeron Lower School 7:50am-2:25pm

Thursday - August 29, 2013 LMS Back to School Night 6:00

PM to 9:00 PM Johnson Performing Arts Building

Monday - September 2, 2013 LABOR DAY- NO SCHOOL

Monday - October 14, 2013 FALL BREAK- NO SCHOOL

Tuesday - October 15, 2013 NO SCHOOL - LMS & PSK In-

Service Day NO SCHOOL - Prep Parents

Conferences

Wednesday - October 16, 2013 PREP TEST DAY

Thursday - October 31, 2013 Halloween Celebration - LMS &

PSK Half Day 7:50 AM to 11:40 AM

LMS Parent Conferences 1:00 PM to 4:00 PM

Friday - November 1, 2013 LMS & PSK NO SCHOOL - Parent

Conferences 8:00 AM to 6:00 PM

Wednesday - November 27, 2013 THANKSGIVING BREAK - NO

SCHOOL

Thursday - November 28, 2013 THANKSGIVING BREAK - NO

SCHOOL

Friday - November 29, 2013 THANKSGIVING BREAK - NO

SCHOOL

Monday - December 2, 2013 CLASSES RESUME

Wednesday - December 11, 2013 FEAST OF GUADALUPE

Friday - December 20, 2013 Dismissal for Christmas Break -

PSK & LMS HALF DAY 11:40 AM Montessori & LMS Campus

Monday - December 23, 2013 CHRISTMAS BREAK - NO CLASSES

Tuesday - December 24, 2013 CHRISTMAS BREAK - NO CLASSES

Wednesday - December 25, 2013 CHRISTMAS BREAK - NO CLASSES

Thursday - December 26, 2013 CHRISTMAS BREAK - NO CLASSES

Friday - December 27, 2013 CHRISTMAS BREAK - NO CLASSES

Monday - December 30, 2013 CHRISTMAS BREAK - NO CLASSES

Tuesday - December 31, 2013 CHRISTMAS BREAK - NO CLASSES

Wednesday - January 1, 2014 CHRISTMAS BREAK - NO CLASSES

Thursday - January 2, 2014 CHRISTMAS BREAK - NO CLASSES Friday - January 3, 2014 CHRISTMAS BREAK - NO CLASSES

Monday - January 6, 2014 CHRISTMAS BREAK - NO CLASSES

SPRING 2014 Tuesday - January 7, 2014 CLASSES RESUME

Monday - January 20, 2014 Martin Luther King Jr. Holiday -

NO CLASSES

Friday - February 7, 2014 PSK No School - Parent

Conferences

Monday - February 17, 2014 WINTER BREAK - NO CLASSES

Tuesday - February 18, 2014 WINTER BREAK - NO CLASSES

Wednesday - February 19, 2014 WINTER BREAK - NO CLASSES

Thursday - February 20, 2014 WINTER BREAK - NO CLASSES

Friday - February 21, 2014 WINTER BREAK - NO CLASSES

Monday - February 24, 2014 CLASSES RESUME

Friday - March 14, 2014 NO SCHOOL - IN-SERVICE DAY

Friday - April 4, 2014 LMS No School - Parent

Conferences

Monday - April 14, 2014 EASTER BREAK - NO SCHOOL

Tuesday - April 15, 2014 EASTER BREAK - NO SCHOOL

Wednesday - April 16, 2014 EASTER BREAK - NO SCHOOL

Thursday - April 17, 2014 EASTER BREAK - NO SCHOOL

Friday - April 18, 2014 EASTER BREAK - NO SCHOOL

Monday - April 21, 2014 EASTER BREAK - NO SCHOOL

Tuesday - April 22, 2014 CLASSES RESUME

Friday - May 16, 2014 PSK - NO SCHOOL Parent

Conferences

Friday - May 23, 2014 Prep Graduation

Monday - May 26, 2014 MEMORIAL DAY - No School

Friday - June 6, 2014 LMS & PSK Half Day

124


FALL 2014 Sunday - August 24, 2014 Freshman Orientation SHP

Campus

Monday - August 25, 2014 Prep Full Day SHP Campus PSK Back to School Night 5:00 PM

to 8:00 PM Montessori and Campbell Center

Tuesday - August 26, 2014 Lower School Half Day 7:50 AM

to 11:40 AM Bergeron Lower School

Middle School Full Day 7:50 AM to 3:15 PM Xie Middle School7:50am-3:15pm

Wednesday - August 27, 2014 Lower & Middle School Full Day

7:50 AM to 2:25 PM Bergeron Lower School7:50am-2:25pm

PSK Orientation 9:00 AM to 1:00 PM Montessori

Thursday - August 28, 2014 PSK Half Day Montessori LMS Back to School Night 6:00

PM to 9:00

Friday - August 29, 2014 PSK Half Day Montessori

Monday - September 1, 2014 LABOR DAY- NO SCHOOL

Monday - October 13, 2014 FALL BREAK- NO SCHOOL

Tuesday - October 14, 2014 NO SCHOOL - Prep Parents

Conferences

Wednesday - October 15, 2014 PREP TEST DAY

Friday - October 31, 2014 Halloween Celebration - LMS &

PSK Half Day

Thursday - November 6, 2014 MIDDLE SCHOOL - NO SCHOOL -

Parent Conferences 8:00 AM to 6:00 PM

Friday - November 7, 2014 LMS & PSK NO SCHOOL - Parent

Conferences 8:00 AM to 6:00 PM

Wednesday - November 26, 2014 THANKSGIVING BREAK - NO

SCHOOL

Thursday - November 27, 2014

THANKSGIVING BREAK - NO SCHOOL

Friday - November 28, 2014 THANKSGIVING BREAK - NO

SCHOOL

Monday - December 1, 2014 CLASSES RESUME

Friday - December 19, 2014 Dismissal for Christmas Break -

PSK & LMS HALF DAY 11:40 AM Montessori & LMS Campus

Monday - December 22, 2014 CHRISTMAS BREAK - NO SCHOOL

Tuesday - December 23, 2014 CHRISTMAS BREAK - NO SCHOOL

Wednesday - December 24, 2014 CHRISTMAS BREAK - NO SCHOOL

Thursday - December 25, 2014 CHRISTMAS BREAK - NO SCHOOL

Friday - December 26, 2014 CHRISTMAS BREAK - NO SCHOOL

Monday - December 29, 2014 CHRISTMAS BREAK - NO SCHOOL

Tuesday - December 30, 2014 CHRISTMAS BREAK - NO SCHOOL

Wednesday - December 31, 2014 CHRISTMAS BREAK - NO SCHOOL

Thursday - January 1, 2015 CHRISTMAS BREAK - NO SCHOOL

Friday - January 2, 2015 CHRISTMAS BREAK - NO SCHOOL

Tuesday - January 6, 2015 CLASSES RESUME

Monday - January 19, 2015

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