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Weatherization And Intergovernmental Programs Office
Benchmarking Energy Data Analysis Crystal McDonald Shankar Earni September 27, 2016
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Technical Assistance to State and Local Governments
Subscribe: http://wip.energy.gov/solutionc
enter or Email Us:
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State and Local Resources for a Clean Energy Future
What you will find: • Targeted opportunities for impact in
your jurisdiction • Key resources from each of the four
action areas • Information on initiatives and
resources in renewable power, sustainable transportation, and energy-saving homes, buildings, and manufacturing from across EERE
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Outline
• Benchmarking - Introduction, Benefits and Process • Data Analysis - Motivation • Benchmarking Data Analysis
– Building Portfolio Analysis • GSF Analysis • Building Type Distribution
– Building Portfolio Energy Analysis • Energy Star • Energy Use Intensity
– Building Level Analysis • Monthly Data • High Frequency Data
• Summary
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Course Objectives
Intended Audience Cities, communities, and states that have implemented or are considering implementing an internal or community-wide benchmarking and/or disclosure program or policy and are preparing their building energy data for analysis. Learning Objectives: Training modules are intended to help public sector organizations: • Analyze benchmarking data to identify facilities that are
underperforming • Help create a targeted approach to improve their energy
performance
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Introduction to Benchmarking
• Benchmarking compares the measured performance of a facility to itself, its peers, or established norms
• Benchmarking provides an empirical foundation for an organization's energy management strategy
• Benchmarking helps manage buildings effectively. You can't manage what you don't measure
• Benchmarking facilitates energy accounting. It assists in identifying opportunities for improvement. It can also help quantify/verify energy savings.
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Benefits of Benchmarking
• Manage energy use proactively • Assess and compare building’s energy performance • Identify billing errors and other anomalies • Verify pre- and post-project energy use, greenhouse gas
emissions, and energy costs • Assess effectiveness of current operations, policies, and
practices • Assist in planning: set goals, targets, and timelines • Communicate results in meaningful terms • Participate in energy challenges or benchmarking programs
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Building Benchmarking Process
1. Developing a Benchmarking Plan
2. Benchmarking Tools
3. Outreach
4. Data Collection
5. Quality Assurance/Control
6. Analyzing & Interpreting Results
7. Communicating Results
Data Cleansing & Analysis
Data Collection, Tracking
Getting Started
Source: http://energy.gov/eere/wipo/building-energy-use-benchmarking
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Benchmarking Data
ENERGY STAR® Portfolio Manager Data Fields
Collects Reports Property Information • Primary function • Location • Vintage Property Type Data • Gross floor area • Property use details (e.g., number
of computers, number of occupants, operating hours)
Energy Consumption Data • Energy data for all fuel usage
Absolute Performance • Total energy (kBtu) • Site and source EUI (kBtu/ft2/year) • Total GHG emissions
(MtCO2e/year) Comparative Performance • National median site and source
EUI • % difference from national median
source EUI • ENERGY STAR score
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Data Cleansing Summary • Data sets are dirty until scrubbed clean
– Accurate data will strengthen confidence in final results
• Promote the use of standardized data dictionary to reduce the effort related to mapping
• Identify missing or suspicious values – If individual data fields seem suspect it may not be necessary to throw
out the whole building record – Very similar energy uses for different building types in different
locations can be an indication of default values
• Use typical ranges to identify data anomalies – Data cleansing may require learning about building performance
characteristics to make judgments on reasonable values – Identify maximum and minimum gross floor areas for each of the
building types from various other sources – Ranges of EUI can be obtained from public sources (CBECS, BPD)
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Data Analysis-Drivers
• What type of buildings consume a big portion of the energy? • What specific buildings have a higher energy use intensity
compared to their peers? • How many of my buildings are improving their energy
performance year over year? • How do I identify buildings that have performance issues that
need attention? • How do I identify energy conservation measures for low
performing buildings? What data is needed to help with this process?
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Data Analysis Process
• Building Type Distribution
• Gross Square Footage
Building Portfolio Analysis
Building Portfolio
Energy Analysis
Building Level Analysis
• Energy Star Score
• Energy Use Intensity
• Monthly Data • High Frequency
Data
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Data Analysis on Sample Datasets
• Cleansed dataset includes benchmarking data for 2,100 buildings over 4 years
• Dataset obtained from FEMP’s Compliance Tracking System (CTS)
Additional sample dataset used: • City of Philadelphia data set
• Data fields: – Facility ID – City – Zip code – Building type – Activity year – Benchmarked floor area – Annual building energy use – Site EUI & source EUI – Weather normalized site EUI – Weather normalized source EUI – ENERGY STAR rating
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Number of Buildings by Type Why? • Understand distribution of building
types • Same types of buildings might have
similar systems and might need similar energy conservation measures
Results Highlights • Total buildings = 1539 with 625 (42%)
classified as “Other” (do not qualify for Energy Star Score)
• Building portfolio dominated by office buildings
• Hospitals, warehouses, and courthouses are additional large categories by building number
Total number of buildings = 1539
Total number of buildings - others = 914
Building Portfolio Analysis
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Floor Area by Building Type Why? • Understand distribution of floor
areas among building types • Develop targeted approach to
address underperforming buildings by sizes, e.g., buildings less than certain size might qualify for certain programs.
Total Floor Area ~500 Million Square feet
Building Portfolio Analysis
Results Highlights • Total floor area ~ 500 mil sq ft • Building area is dominated by office buildings, followed by hospitals, and courthouses,
Non-refrigerated warehouses • “Other” floor area ~ 96 mil sq ft Insights • “Other” represents 42% of buildings by number but occupies less than 15% of the area,
making this a smaller opportunity than analysis by number indicates alone; • While hospitals represent 7% of buildings by number, they account for nearly 17% of the
area, increasing their importance relative to the overall portfolio
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Number of buildings & Energy Consumption by Size
Why?
• Pareto principle (80-20 rule) • Larger buildings (>250K GSF) tend to have very different
mechanical system types (e.g., a centralized heat or chiller plant) than smaller buildings (e.g., packaged rooftop or window units)
• Large buildings typically have a greater impact potential on energy consumption than small buildings, on a per building basis
Results Highlights
• More than 3/4 of the buildings are small (<250K GSF) • Large buildings (>250K GSF) make up 26% of all
buildings but consume a more significant portion of the energy (~83%)
Insights
• Design a tailored strategy to address the different building sizes to improve their performance. For example large buildings might be good candidates for ESPC projects.
Building Portfolio Analysis
Energy Number
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Small and Large Building Types by Number Why? • Identify buildings that can take of advantage of
specific programs geared towards a specific building type (e.g., Data Center Challenge)
• Larger buildings typically have a greater impact potential on energy consumption
• Identifying types of large buildings will help us design a targeted approach to improve their performance
Results • Large buildings (>250k SF) are dominated by offices,
hospitals, courthouses, “Other” and non refrigerated warehouses
• Small buildings are dominated by “Other” and offices, followed by non refrigerated warehouses, residence halls and K-12 schools.
Insights • Offices represent a significant fraction of large
buildings, therefore can provide an attractive opportunity for improvement
• Small buildings represent a more diverse population because of the larger “other” component.
Building Portfolio Analysis
Large Buildings (796)
Small Buildings (2306)
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Distribution of Building Sizes by Type Why? • Helps to identify the distribution of building
sizes individually by type • Separating the buildings by their type and area
will help develop targeted approaches to address performance issues
• For example offices below a certain size might be eligible for some special utility programs
Results • Office buildings occur in all sizes • K-12 schools, dormitories, retail and service
buildings are smaller than 250K SF • Courthouses and hospitals are typically larger
than 250K SF Insights • Addressing inefficient courthouses, hospitals
and office buildings might be a good strategy to get the most savings with less resources.
Building Portfolio Analysis
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Total Energy Use by Building Type Why? • Understand distribution of energy usage by
building types • Help focus on buildings types that
consume the most energy • Certain buildings are more energy intensive
than others – hospitals, data centers, etc. Results • Total energy use is dominated by hospitals
and office buildings. Courthouses represent a smaller opportunity, using less than 6% of total energy
Insights • Even though office buildings are greater in
number and occupy more area, hospitals consume more energy
Site Energy Use
Building Portfolio Energy Analysis
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Energy Star Score Analysis Why • Understand distribution of energy star
scores (ESS) for the full building portfolio • Low performing buildings (ESS<50) should
be targeted for improvement and will generally have the best rate of return on investment
Results • There are 224 buildings with an ESS of 50
or less whose performance needs to be improved
• There are about 282 buildings with an ESS of 75 or better
Insights • Low performance buildings (ESS<50) need
to be analyzed in more detail to understand and design a more targeted approach to improve their performance
Building Portfolio Energy Analysis
Source: EPA
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ESS Analysis by Building Types Why • Understand distribution of energy star
scores by building type • Identify low performing buildings and
their types (ESS<50) Results • There are 166 office and 32 hospital
buildings with energy star scores of 50 or less
Insights • There is a wide distribution of scores for
offices, hospitals and non-refrigerated warehouses, whereas most schools are high performers
• Courthouses, which occupy 10% of the total area and 5% of the energy consumptions and costs are performing well relative to other building types
Energy Star Score
Building Portfolio Energy Analysis
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Distribution of ESS by Building Types Insights • The median ESS for each
building type is above 50, however some building types have very wide distributions, with many buildings scoring below 50. This is particularly the case for hospitals and offices
• Courthouses and schools are relatively high performing buildings with a high median ESS and very tight spread among the buildings
• Identify and focus on underperforming office and hospital buildings with ESS<50 for biggest impact
Building Portfolio Energy Analysis
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Energy Star Score by Building Size
Results • The median energy star
scores for all buildings <200K SF is around 50 with lot of variation
• The median ESS for all buildings >200K SF is greater than 70.
• Buildings >800K SF are high performing buildings with a high median ESS and very tight spread among the buildings
Insights • Identify and focus on
underperforming buildings with GSF <600K SF with ESS<50 for highest impact
Portfolio Analysis
GSF (in 1000s Sq. Ft)
Building Portfolio Energy Analysis
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Energy Use Intensity (EUI) Analysis
What? • A good number of buildings in the data
do not have an ESS – either the buildings are not eligible or some of the required key variables are not entered in Portfolio Manager
• Understand distribution of Site EUI for building stock with no ESS
• Helps identify low performing buildings – Compute the mean and standard
deviation for Site EUI for entire population of buildings
Outliers – Site EUI > Mean + 3 Standard Deviation
Insights • There are 68 buildings that have a Site
EUI > 375 whose performance needs to be investigated
Building Portfolio Energy Analysis
EUI 375
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Distribution of Site EUI by Area Why? • For buildings that do not have an ESS, Site EUI can help us understand the building’s
absolute energy performance and conduct comparisons • Understand the relationship between Site EUI and building area Insights • A significant number of underperforming buildings are small that are less than 200KGSF • However, addressing low performing large buildings may result in more per capita
energy savings
In 1000s S. Ft
Building Portfolio Energy Analysis
EUI 375
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67
29
93
197
74
58
79
40
62
196
44
71
0
50
100
150
200
250CBECS Median
Sample Median
Building Type Site EUI relative to CBECS
What? • Site EUI is an indicator of the
absolute performance of the building
• For buildings with no ESS, the relative performance of the building can be compared to established benchmarks (CBECS)
Insights • Offices, Non-refrigerated
warehouses, and K-12 schools are underperforming relative to median CBECS (2003)
• The median EUI of courthouses and residence halls are lower than the CBECS median
• Hospitals are performing at par with CBECS peers.
Site EUI (KBtu/SF)
Building Portfolio Energy Analysis
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Site EUI by Building Types
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
0
50
100
150
200
250
Coefficient of Variation
Mea
n an
d St
anda
rd D
evia
tion
(Site
EU
I)
MeanStandard DeviationCv
• Knowledge of the variability will help to identify the lowest performing buildings. Standard Deviation and Coefficient of Variation are indicators of the variability
Results • Site EUIs for Office, Non refrigerated
warehouses, hospitals and K-12 schools buildings have considerable variations amongst themselves (Cv>50%)
• The Site EUI for courthouses and residence halls not only have better median than CBECS but also their variations is comparatively low.
Insights • Focus on buildings with high Cv (>50%)
and high mean and median - Office, K-12 Schools, non-refrigerated warehouses
Building Portfolio Energy Analysis
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0
50
100
150
200
250
300
20 40 60 80 100 120 140 160 180 200 220 >240
Num
ber o
f Bui
ldin
gs
Median (CBECS) 67.3 kBtu/ft2
Median (Sample) 78.7 kBtu/ft2
Insights The median Site EUI for CBECS is lower than the sample median, indicating that the overall portfolio of office buildings may be less efficient than CBECS A third of the office buildings are underperforming compared to the median.
Site EUI Distribution for Office Buildings Building Portfolio Energy Analysis
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0
5
10
15
20
25
30
35
40
Num
ber o
f Bui
ldin
gs
Median Site EUI (Sample) 34 kBtu/ft2
Site EUI Distribution for Warehouses
Median Site EUI (CBECS) 28.5 kBtu/ft2
Insights • Around 20 warehouses report a site EUI of
greater than 100 kBtu/sft. • The high EUI numbers may be due to
classifying the warehouses as non-refrigerated when they should be classified as refrigerated
Building Portfolio Energy Analysis
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0 2
15
59
46
18
12
5 2
0 0
10
20
30
40
50
60
70
15 30 45 60 75 90 105 120 135 More
Num
ber o
f Bui
ldin
gs
Site EUI (Kbtu/sft)
Median (Sample) 61.5 kBtu/ft2
Site EUI for Courthouse Buildings
Median (CBECS) 93.2 kBtu/ft2
There are 19 buildings with Site EUI greater than the CBECS Median Site EUI of 93.2. There are 2 buildings with Site EUI of 30 or less.
Building Portfolio Energy Analysis
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Site EUI for Hospitals Median (CBECS) 196.9 kBtu/ft2
• Median Site EUI (CBCES)~Median Site EUI (Sample), indicating very similar distribution profiles
• 5 buildings with Site EUI >400 (3σ)
Building Portfolio Energy Analysis
Median (Sample) 201.1 kBtu/ft2
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Benchmarking Data Analysis Summary
• A significant number of buildings are classified as “Other” which can be a combination of different kinds of buildings
• The building portfolio is dominated by offices both by number and area. Hospitals however, are the largest consumers.
• There is a wide distribution of Energy Star scores for offices, hospitals and non-refrigerated warehouses, indicating a range of performance. Focus on the underperforming buildings (with low scores) to prioritize resources.
• Large buildings (>250K) make up only 26% of all buildings yet consume a significant portion of the energy (~83%)
• A significant number of underperforming buildings are smaller than 250K SF in area
• Offices, Non-refrigerated warehouses, courthouses and K-12 schools are underperforming relative to median CBECS (2003)
• Focus on Offices, Non-refrigerated warehouses, courthouses and K-12 schools that are smaller than 250K in GSF with ESS<50 and Site EUI > 375 kbtu/sft
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Building Level Analysis
• Energy consumption for most buildings can be described by models that relate energy consumption to independent variables
• Change point models are used for predicting energy consumption as a function of outside air temperature
• Models can detect statistically significant changes in building performance over time - changes in building /equipment use or load, performance degradation due to improper operations
• Helps to identify energy conservation opportunities • Monthly utility data analysis for some buildings can establish
benchmarks for weather and non weather related consumption • High frequency utility data analysis (15 min) can help establish a
daily profile that can be used for comparisons and identifying anomalies in operation
Building Level Analysis
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Monthly Energy use as a function of outside temperature
3 Parameter-Cooling Change-Point Model: β1 + β2( X1 – β3) 3 Parameter-Heating Change-Point Model: β1 + β2( X1 – β3) where: β1 – intercept indicates energy consumption that’s non weather dependent Β2 - parameter defining temperature dependency (slope) Β3 - the change-point X1- outside air temperature
Change-Point Models Energy use vs. outside temperature
Building Level Analysis
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Model Predictions using high frequency data
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Energy consumption predictions (red) using the 15 minute data as a function of temperature and time of week compared to actual reported numbers (black) Insights • This model can help to quickly identify any future anomalies in the
performance of the building- high weekend load, equipment being left on during unoccupied hours
Building Level Analysis
Data needed: 15 minute interval time series electric data, outside air temperature
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High Frequency Utility Data Analysis
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• Plot below shows the energy load in 15 minute intervals for September 2009
• Effective controls to turn off unnecessary loads during nights and weekends as evidenced by very low base-load of 0-30 kW shown in purple.
Building Level Analysis
• Plot above shows the energy load in 15 minute intervals for September 2008
• Load NOT being turned off during nights and weekends due to lack of controls as evidenced by high base-load of 60-90 kW shown in green.
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High Frequency Utility Data Analysis
• Baseline operating monthly profile (June) established using the model (No issues).
• Comparing this model with the operation in the months of July and August, this building fails to shut down at night (shaded periods).
Building Level Analysis
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• High building resting load • Occupancy hours need adjustment • Setback points not enabled • Continuously running equipment
• High loads during occupancy • Set points can be tweaked
• Plot shows the baseline gas consumption data (blue) where the heating is triggered when the OAT<68 F
• Plot shows the post retrofit gas consumption data (red) after the controls are adjusted to trigger the heat when the OAT<60 F
• Building envelope needs sealing • AHU economizing not enabled • Simultaneous heating and cooling
Energy Conservation Opportunities Building Level
Analysis
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Summary
• Benchmarking a portfolio of buildings results in large amount of data, that can provide valuable insights to: – Understand building stock by building types their number, size and energy usage – Identify underperforming buildings with each size and size category
• Focus on the underperforming buildings (80/20 rule) that will attain the biggest return on investment – High energy consuming building types – Highest Site/Source EUI compared to its peers – Lowest energy star scores – Biggest drop in performance year over year
• Building level analysis – Monthly utility data analysis
• High HVAC energy consumption – High frequency data analysis
• Understand issues with day to day operation (lack of night setback, equipment being left during unoccupied hours, etc.)
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Resources • ENERGY STAR Portfolio Manager
– http://www.energystar.gov/buildings • Standard Energy Efficiency Data Platform (SEED)
– http://energy.gov/eere/buildings/standard-energy-efficiency-data-platform • Building Energy Data Exchange Specification (BEDES)
– http://energy.gov/eere/buildings/building-energy-data-exchange-specification-bedes
• Benchmarking Data Cleansing: A Rite of Passage along the Benchmarking Journey, April 2015 http://energy.gov/eere/slsc/downloads/benchmarking-data-cleansing-rite-passage-along-benchmarking-journey
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Questions
Contact: Shankar Earni, Ph.D. Lawrence Berkeley National Laboratory [email protected] Office: 510-486-7126
