Sergio Tarantino

CIFE Seed Proposal FY13-2018

Assessment and reduction of the risks inherent in Performance-Based Contracting by use of CIFE’s

automated Building Energy Model generator

Enabling Performance-Based Contracting

Designers, building owners and operators lack methods for assessing the impact of building operations on building performance as predicted by design-stage energy models.

PBC has been fraught with unacceptable risks of performance and costs uncertainty due to misalignment between predictions and measured energy consumption.

The research purpose is to assess the risk involved in using Building Energy Performance Simulation (BEPS) as a foundation for Performance Based Contracting (PBC) on new and existing buildings

The solution investigated is an automated rapid method to create an accurate Building Energy Model (BEM), coupled with a method to quantify the parametric error associated

Benefits of PBC

Building owner

• Zero investment costs• Zero O&M• Predictable energy costs• Guaranteed High-performance

building• Guaranteed building comfort• Customizable requirements

definition1

• Outsource active systems(HVAC as a service)2

Performance contractor

• Optimize energy delivery• Control over the building-systems integration• Capacity sharing across multiple contracts2

• Lower servicing costs1

• Reduce cost of comfort delivery1

• Improve resources utilization• Opportunity for innovation2

• Improve customer acquisition for highly innovative technologies2

• Improve customer loyalty1

1 Strien J van, “The handling of risks induced by performance-based contracting in service supply chains”, Sep 20162 Franconi, Nelson, “Risk-Based Building Energy Modeling to Support Investments in Energy Efficiency”, ACEEE, 2012

Performance-Based Contracting (PBC):

Agreement between owner and a third party

energy contractor to provide a level of building

performance within defined comfort boundaries

Building Owner

Performance Contractor

Defines requirementsPays monthly fee for building performance

Meets owners’ requirementsDelivers service of providing building comfortOperates the building efficientlyMaintains the mechanical systems

Precise estimation of energy performance

Reduction of uncertainties to minimize risks

BEPS model

Risk Assessment

How does PBC work? How is PBC possible?

BEPS model

Risk Assessment

Risks to PBC adoption

High Opex

High servicing costs

Building Owner

Performance Contractor

Uncertainty of energy performance estimates

No quantification of the impact of risk factors

Task#2: Risk assessmentTask#1: BEPS model generation

Raw Point Cloud Data

BEPS geometry

Non-geometrical parameters

BEPS Model

thermal/material properties, HVAC, building loads, occupancy

Accuracy anduncertainty

quantification

Financial Uncertainty

Analysis

Performance Based

Contracting(PBC)

simulation results

Scan-to-BEPSinput

parameters

Research framework

In order to enable Performance-Based Contracting

we investigated

the sources of discrepancy betweenassessed prediction and measurement

of building energy performance(“Performance Gap”)

Where is the performance gap coming from

ExpectedEnergy Use

+ 10-70% + 10-30% + 15-30% + 30-120%

ActualEnergy Use

typically +150%

ConstructionDesign Commissioning Operations

Assessing underlying causes of the performance gap

Design assumptions

Energy modelling tools

Built quality

Poor commissioning

Management and controls

Occupancy behavior

Construction

Design

Commissioning

Operations

10-70%

20-60%

<20%

<20%

15-80%

10-80%

Estimated quantitative

effect on energy useUnderlying Cause

1. Design assumptions

2. Modelling tools

3. Management and controls

Design

Operations

40-85%

20-60%*

15-60%

Measured improvement of

BEP predictions’ accuracy

on CV(RMSE)Field of investigation

* research validation in progress

BEPS predictions’ accuracy improvement over

the last 2 years of research and development

15 case studies

- 7 office buildings

- 3 restaurant

- 3 retail

- 2 theatre

Size (avg)

150,000 sqft

50,000 sqft

80,000 sqft

200,000 sqft

performance gap

171% (avg)CV(RMSE)

1. Assessment of BEPS design assumptions [DESIGN]

“Parametric analysis of design stage building energy performance simulation models”, P.Shiel, S.Tarantino, M.Fischer

“Parametric analysis of design stage building energy performance simulation models”, P.Shiel, S.Tarantino, M.Fischer

98%

13%0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

before after

Energy Usage CV(RMSE)

HVAC Lighting Loads Whole Building

BEP predictions’ accuracy improvement: + 68% avg*

compared to best energy model available at the end of design stage

Hundreds of errors Manually re-entering building information

Missing

spaces/elements

Dislocated

elements

Space-

Element

Alignment

Incorrect

normal vector

BIM

construction-oriented details

3D elements

no seamless transition

time consuming

extensive manual post-processing

BEPS

simplified geometry

2D surfaces

2. Modeling tools [DESIGN]

Automated LaserScan-to-BEPS input geometry

3 CASE

STUDIES

3 more planned next quarter

Geometry specification

+

Modeling simplification

+ 40% (avg)

BEM geometry

accuracy*

Impact on BEP assessment

- 90%

time required to

develop BEM

* research validation in progress

Analysis of building

management mismatches

between design

assumptions and actual

operations

+ 45%

BEM prediction’s accuracy *

- 180% of building

energy consumption **

Impact on BEP assessment

3. Management and Controls [OPERATIONS]

* per 20,000sqft Office Building **average from Building actual operations to improved operations based on BEM predictions

A. Unregulated Loads

B. Uncertainty quantification

C. Performance feedback loop

(design-operations)

Design

Operations

30-60%*

> 60%*

> 60%*

Expected improvement of

BEP predictions’

accuracyField of investigation

* expectations based on data available from literature review

Next tasks

Capacity for new case studies

- Laboratory

- Retail

- Hospital

- Office

- Other commercial building

Life-cycle

Goal: Predictions’ accuracy > 95%

Research framework validation

Thank you

Questions?

Appendix

Project delivery

ConstructionDesign Commissioning Operations

Implications of

design changes on

energy use

considered

• Test proposed

changes as to their

effect on eventual

building performance

Systems are

commissioned

appropriately

• Complete and rigorous

commissioning – in

some instances

seasonal

commissioning

Buildings are

managed for

minimum energy

use

• BMS and control audits

to ensure once the

building is running at its

optimum it stays there

Sta

nd

ard

Pra

cti

ce

Simplification and

inaccurate

assumptions in

design process

Value engineering

design changes

and poor quality of

construction

Inadequate, rushed

or incomplete

commissioning of

systems

Systems not operated

as intended/envisaged

• Design modelling naïve

• Assumed perfect

controls

• Value engineering

changes

• Contractor designed

elements

• Quality of construction

• Commissioning

rushed or incomplete

• BMS controls not

working as intended

• Unmanageable

complexity of systems

• User over-ride of BMS

• Poor energy

management

Reality

Assumptions in

design process

reflect real

anticipated operation

• Feedback to briefing

and early design is

crucial

• Energy models reflects

practice including use

and the nuances in

controls

?

CODE COMPLIANCE

ASHRAE 90.1BASELINE

building energy

consumption

design stages

good design

poor design

high performance

poor performance

DESIGN OPERATIONS

x

x

Regulated

Energy 31%

69%

Un-Regulated

Energy

Energy

consumption

Building Energy Model (BEM)

A. BEM after code compliance

B. Building operations

Anticipate the impact of building operations on energy performance predictions through:

- Assessment of the impact of building operations assumptions variability on predictions

- Identification of the input parameters associated with building operations affecting the energy performance predictions

- Generation of predictions for the anticipated range of building operations

C. Performance feedback loop (design-operations)

Building Energy Performance Simulation (BEPS) tools

Design assumptions

not transferred to

operations

Operations deviate

from design intent

Design

energy model

Assumption

s

Predictions

Operations

Actual

building

operations

Actual performance

energy model

Case study

Max indoor temperature: 26°CMin indoor temperature: 20°C

Maximum Relative Humidity: 80%Minimum Absolute Humidity Ratio: 0.03

Maximum indoor CO2: 800ppm

Energy Use Intensity (EUI) < 65 kBTU/ft2/yr

14,000 sqft office buildingLocation: San Francisco

Title24 CompliantLEED Platinum

Performance-based contract

Active system

Capex: $0O&M: none

Comfort1

Owner Requirements:

IAQ2

Energy3

Required Compliance: > 95%*

1ASHRAE Standard 55 – Thermal Environmental Conditions For Human Occupancy2ASHRAE Standard 62.1 – Ventilation and Acceptable Indoor Air Quality in Low3ASHRAE Standard 105 – Standard Methods of Determining, Expressing, and Comparing Building Energy Performance and Greenhouse Gas Emissions* No less than 346 days a year. Performance not achieved for max 428 hours over a year

O&M as a serviceOpex: 4,8 [$/sqft*year]

Fixed fee for guaranteed building performance Guaranteed low-energy consumption

Guaranteed occupants comfort

Owner’s cashflow over 10-year

* The Building Owners and Managers Association (BOMA) International Reports “Utilities + Repair, Maintenance: 1.63 + 2.10 = 3.73 $/sqft/year” average for Office buildings in the US

cashflow comparison PBC vs no-PBC

PBC

no-PBC

Capex: $0Opex: 4,8 $/sqft*yearCompliance: 95% avg

Capex: $750kOpex: 3,2 $/sqft*yearCompliance: 48% avg