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Contract No. DE-AC36-08GO28308

Using Models to Provide 

Predicted Ranges for BuildingHuman Interfaces

Preprint

Nicholas Long, Jennifer Scheib, Shanti Pless, 

and Marjorie Schott

National Renewable Energy Laboratory

Presented at the 2013 Building Simulation Conference

Chambéry, France

August 25-28, 2013

Conference Paper

NREL/CP-5500-58521

September 2013
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USING MODELS TO PROVIDE PREDICTED RANGES FOR 

BUILDING-HUMAN INTERFACES

Nicholas Long, Jennifer Scheib, Shanti Pless, and Marjorie Schott

National Renewable Energy Laboratory, Golden, CO, USA

ABSTRACT

Most building energy consumption dashboards provide 

a snapshot of building performance; others provide 

detailed historical data for comparison to current usage.

This paper discusses the Building Agent platform, 

which was developed and deployed in a campus setting 

at the National Renewable Energy Laboratory as part 

of an effort to maintain the aggressive energy 

performance achieved in newly constructed office 

buildings and laboratories.

The Building Agent provides aggregated and coherent 

access to building data, including electric energy, 

thermal energy, temperatures, humidity, and lighting 

levels, and occupant comfort feedback, which are 

displayed in various manners for visitors, occupants, 

facility managers, and researchers. This paper focuses 

on the development of visualisations for facility 

managers, or an energy performance assurance 

provider, where metered data are used to generate 

models that provide live predicted ranges of building 

performance by end use. These ranges provide simple, 

visual contexts for displayed performance data without 

requiring users to assess historical information or 

trends. Several energy modelling techniques were 

explo red, including static lookup-based performance 

targets, reduced-order models derived from historical 

data using main effect variables such as solar radiance 

for lighting performance, and integrated energy models 

using a whole-building energy simulation program.

This paper describes the new building construction 

backdrop that has motivated this work, the system 

architecture providing access to building data, the 

various modelling approaches currently employed, and 

the visualisation methods used to display performance 

and modelled data.

INTRODUCTION

As net-zero energy building procurement moves out of 

infancy, the endeavour expands from realizing 

aggressive energy goals in design to maintaining 

designed performance over the life of the building.

Changes in occupancy patterns, miscellaneous loads, 

and installed equipment performance can significantly 

impact year-to-year energy performance. In one 

building lifetime scenario, energy use increases over 

time as equipment is added or operational schedules 

change. In another, energy use decreases over time as 

equipment degrades or fails but at the sacrifice of 

occupant comfort. The ideal scenario is when 

equipment is maintained and upgraded appropriately, 

which reduces energy consumption and maintains 

occupant comfort continually (accounting for variations 

in weather and other external factors). To ensure the 

preferred scenario, an interface between the building, 

the occupants, and the operational staff is needed for 

fact-based, proactive decision-making. 

Like energy models in design, dashboards have 

surfaced as the primary interface between the building 

and the decision makers. Many dashboard 

visualisations are paired with a single energy goal, such 

as net-zero energy, which is sufficient if no problems 

are encountered but does not provide direction for 

corrective action if the goal is not met. The premise of 

this paper, then, is that the dashboard must not 

oversimplify the design-based energy model results but 

rather disentangle them for ongoing analysis by 

building decision makers. The result is a more complex 

dashboard architecture and a deeper understanding of 

the building’s operation; both outcomes will be 

illustrated through the U.S. Department of Energy’s 

(DOE) National Renewable Energy Laboratory 

(NREL) campus dashboard and occupant interface, the 

Building Agent (BA).

NREL Campus Background

The DOE/NREL goal for the campus is to expand its 

leadership as a state-of-the-art laboratory that supports 

innovative research, development, and 

commercialization of renewable energy and energy 

efficiency technologies that address the nation’s energy 

and environmental needs. This growth has resulted in a 

significant increase in employees and facilities on its 

327-acre main campus in Golden, Colorado.

To support this growth over the last five years, NREL 

developed and demonstrated new construction 

procurement methods that proved cost effective and 

showed that 50% energy savings versus typical 

building code are possible when design-build teams 

integrate to achieve specific and measurable wholebuilding energy goals. NREL facility growth provided

an opportunity to demonstrate the integrated approach 
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in real projects by incorporating energy performance 

specifications into the projects’ contracts. NREL 

developed and piloted this energy performance-based 

design-build process in 2008 with the Research Support 

Facility (RSF I), an 824-occupant, 220,000-ft2 office 

building. The process has since been replicated in other 

campus projects such as an office wing expansion, the 

Research Support Facility II (RSF II), a smart grid 

laboratory, Energy Systems Integration Facility, a 

parking structure, and a site entrance building.

Each project incorporated world-class efficiency 

strategies using contractual energy use requirements in 

the design-build contracts, all on typical DOE 

construction budgets. In addition to general energy 

reduction goals such as a 50% reduction versus typical 

building code and sustainability goals such as 

Leadership in Energy and Environmental Design 

Platinum certification, the contract included specific 

and directly measurable energy use requirements:

• RSF I: 36 kBtu/ft2

/yr (11 kWh/m2/yr) energy use 

intensity (EUI), including the data centre and 

net-zero energy

• RSF II: 33 kBtu/ft2

/yr (10 kWh/m2/yr) EUI and 

net-zero energy

• ESIF: 27 kBtu/ft2

/yr (9 kWh/m2/yr) EUI for the 

office space, and 1.06 power usage effectiveness 

(PUE) for the data centre

• Parking Structure: 175 kBtu/parking space/yr 

(51 kWh/parking space/yr)

• Site Entrance Building: 32 kBtu/ft2

/yr (10 

kWh/m2

/yr) EUI and net zero energy

Each project’s design-build team successfully designed

and constructed these buildings to reach the contractual 

energy goals, substantiated by energy models and 

submetering during the first year of operation. NREL 

terms this contractual energy goal substantiation effort, 

combined with energy goal maintenance over the life 

of the building, energy performance assurance.

Energy Performance Assurance

In each of NREL’s recent campus construction 

projects, whole-building energy models were started in 

the pre-design phase by each proposing design-build 

team to prove that its design concepts were likely to 

meet the contractual goals (Pless, Torcellini, Shelton, 

2011). The design-based energy models evolved over 

the design phases and were updated based on 

constructed reality. This energy performance assurance 

task differs from a typical design process in that 

information from the design-based energy model was 

used to make iterative decisions about the building and 

understand cost and energy performance trade-offs 

versus simply verifying that design concepts perform as 

expected at the end of design. 

The first step in energy performance assurance is to 

evolve energy models throughout the design and 

construction. Specifically, this means continually 

refining building geometry, weather input, occupancy 

schedules, etc. For the NREL campus, energymodelling reports were provided at each phase of 

design and as energy-cost trade-off questions arose, 

with clearly identified variables such as input schedules 

that could be discussed among the integrated owner, 

design, and construction team.

The second step is submetering. This requires the team 

to consider the electrical design in order to layout 

meters appropriately to make end use aggregation 

available for validating the design-based energy 

models. For each NREL project, submetering was a 

contractual requirement beyond the implicit contractual 

need to verify the energy goal at occupancy.

These first two steps are critical to setting up a 

building-occupant interface such as a dashboard that 

allows for on-going, proactive decision-making, as the 

evolving energy model allows for in design.

The final step in energy performance assurance, and the 

focus of the remainder of the paper, is the setup of the 

building-occupant interface. This effort includes 

defining the system architecture for data collection and 

designing system visualisations that allow occupants—

including facility managers—to make goal-driven 

decisions. 

Building Agent is NREL’s building-occupant interface, 

developed in tandem with the recent campus 

construction. The following BA description focuses on 

the RSF because the building has been operational for 

more than one year, providing a fuller picture of the 

effort required to maintain energy performance. The 

RSF was the living laboratory for BA development.

SYSTEM DESIGN

The BA scope extends beyond a typical energy

dashboard; it collects and displays energy performance 

data and allows for analysis and investigation into the 

balance of energy use, energy costs, and comfort by 

facility managers and typical occupants.

Building Agent Architecture

The BA architecture consists of four layers: hardware 

and protocols, databases, applications, and 

visualisations. Figure 1 defines the layer components 

and colour codes them according to those developed 

uniquely for BA (“Building Agent” and 

“Visualisations”) and a shared campus resource 

(“DataBUS”).

The first layer addresses the spatially distributed and 

varied protocols for monitoring energy- and comfortrelated events on campus. Elements of this layer 

include (but are not limited to):
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• Power meters such as lighting, mechanical, 

miscellaneous loads, elevators, and photovoltaic 

(PV) panels (Modbus protocol) on each building 

subsystem

• Flow meters on hot and chilled water (BACnet

protocol)

• Spatial environmental sensors such as 

illuminance, temperature, and humidity sensors 

at individual workstations

• Other protocols such as weather station sensors,

including global horizontal and vertical 

irradiance, dry bulb temperature, and wind speed 

and direction

• Client applications deployed to collect occupant 

feedback. Occupants are considered meters with 

many sensors in the BA architecture.

Figure 1: BA Architecture

As a second layer, all the devices either send data or 

are polled at a time interval appropriate to the rate of 

change of the condition being measured. For example, 

environmental sensors send information when 

occupants at workstations give feedback about their 

comfort or every 10 minutes. The power meters are set 

to send data at one-minute intervals. All these data are

collected, cleansed, aggregated, and tagged in two 

databases for use by the BA applications. One 

database, DataBUS, houses raw building performance 

data. These data are open and available for a variety of 

NREL research and assessment needs. A second 

database, specific to and named for BA, houses 

metadata for the raw DataBUS data used for campus 

dashboards. The BA database also segregates private 

occupant data such as comfort feedback. 

The third and fourth layers of the BA architecture go 

hand-in-hand. The data are meaningfully organized and 

manipulated and then displayed via a series of 

dashboards. The up-to-date status report from the 

building to the occupant allows for a human-in-theloop control scenario. For the RSF, the interface takes 

the form of data visualisations through a desktop client 

application, a campus display, and a website. Each

instance is meant to connect with occupants in different 

scenarios but all communicate the RSF’s current 

performance and clearly indicate when the building is 

not meeting expectations. 

The design team was contractually required to deliver a 

dashboard for the RSF (see Figure 2). It used the 

submetered data from the power quality and flow 

meters, but the data analysis layer was not included in 

the architecture. This means that there was no context 

for the displayed energy performance. Further research 

into historical information was necessary for 

performance assurance tasks such as verifying the 

building’s contractual energy goal was being met or 

determining the appropriate course of action if the 

building was not meeting the goal.

Figure 2: Original, Control Vendor-Provided 

Dashboard for the RSF (July 18, 2011, sunny day)

The second iteration of the RSF dashboard design 

added the data analysis layer. Over the first year of 

building operations, the metered data were collected 

and compared to the energy model results at the wholebuilding and end-use scales, as is done in an enhanced 

measurement and verification scope (Sheppy 2013). 

Tweaks were made to building systems and meters as 

needed to ensure that the RSF’s energy performance 

met the contractual energy goal. A new interactive 

dashboard (see Figure 3) was developed that displayed 

the building’s power profile as a breakdown of the 

whole-building energy goals. 

Figure 3: BA Dashboard for the RSF (whole building, 

May 15, 2013, partly cloudy day)

Protocols Databases Applications Visualizations

Building Agent 

Web App

Databus BA Database (private data, metadata, control variables)

Visitor 

Dashboards

Site 

Operations

Building’s 

Dashboards

Detailed 

Data

Client App

(phidgets)

Modbus BACnet Visualization

Building Agent

Databus

Legend

Other 

Protocols
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The interface enables users to “drill down” to inspect 

individual end use time series data as shown in Figure 

4.

Figure 4: BA Dashboard for the RSF (data centre 

power consumption (kW), February 13, 2013, energy 

use not within predicted range)

These examples use power and energy readings to 

display different aspects of performance. Both are 

necessary depending on the metric for comparison; 

multiple dashboard views provide the diversity of 

information occupants and building engineers need to 

make informed decisions about comfort and energy 

use. Although the fourth layer (building-occupant 

interface, display selection and design) is an important 

discussion point for meaningful and actionable 

visualisations (Schott et al., 2012), the remainder of 

this paper addresses the third, data analysis layer.

Predicted Range Approaches

A unique feature of the RSF dashboard, shown in both 

Figure 3 and Figure 4, is the display of dynamic, 

acceptable ranges around subsystem goals. Subsystem 

goals are important for creating a useful dashboard 

because it is rare that at any given point in time, the 

annual energy use or power profile will match the exact 

goals. Three options and the associated steps for 

determining the predicted ranges of whole-building and 

building subsystems are:

1. Lookup tables (no asset or process model available)

Lookup tables can be used for energy goal comparison 

when no model data are available.

• Start with the whole-building energy goal or first 

year aggregation

• Use public energy end use breakdown 

information (DOE, 2010) to divide the total 

operational energy use among end uses

• Use case studies, rules of thumb, and design 

guide support documents (Leach, Lobato, 

Hirsch, et al. 2010) to determine a range of 

acceptable energy use

The advantages to using lookup tables are that they can 

be quickly developed, they do not require initial 

metered data, and they allow a building to be compared 

to industry standards. The disadvantages are that a 

specific system’s energy use might be unrealistic to 

take action to make the buildings end use fall in range. 

Also, the ranges will be static and will not account for 

occupancy or weather variation.

2. Reduced-order model (based on asset model)

A reduced-order model provides quick results based on 

only a few independent parameters such as time-ofday, outside temperature, and irradiance. Typically, the 

manual development of a simple reduced-order model 

would include: 

• Starting with the first year metered data and 

calibrated annual energy model results (and run 

with updated weather)

• Plotting the data sets to determine if the 

measured data aligns with the modelled data

• Ensuring, through data inspection, that the 

compared sets are equal in time period, units, 

and resolutions, and determine the acceptable 

energy use and power profile ranges. 

Specifically, determine whether the design 

predictions are being met. If not, make this the 

high end of the range. If the predictions are 

being met, determine whether the operating 

energy use can be ratcheted down further based 

on occupant comfort feedback or consistent 

occupancy profiles. This can be an automated 

process, but is often an art in current practice.

• Determining the necessary metrics for the 

dashboard to give occupants a complete picture 

of the building’s performance. One metric 

should be the whole-building energy goal such 

as an EUI, but other options include net-zero 

energy, PUE, lighting power density, or less 

specific power profiles.

• Using modelled or measured data, whichever is 

determined to be the appropriate performance

range. 

• Plotting the data sets against a variety of factors 

such as exterior temperature and time of day. 

This can be automated using mutual information 

techniques.

Lookup tables can be used in this approach as a 

variation on the reduced-order model, when the most 

influential parameter can easily be broken down into a 

small number of discrete steps such as time of day. 

Lookup tables are also useful when an operational 

model is unavailable or operations are changing over 

time, but the energy use depends on complex control 

algorithms. 

The advantage of this approach is that the ranges will 

be dynamic, true to the building operations, and will

prevent unrealistic ranges from being displayed in the 
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visualisation. The disadvantages are that an initial data 

collection and review period is needed before the 

dashboard can be deployed, preferably by an energy

engineer. This approach is currently implemented in 

the RSF BA.

3. Real-time simulation (using an operational model)

Perhaps the most sophisticated method of synthesizing 

dynamic performance predictions utilizes real-time 

simulation. This requires:

• Creating an operational energy model that is a 

combined forward (fundamental or reducedorder calculations) and data-driven (e.g., 

metered data-derived occupant schedules) 

approach

• Creating schedules that are true to the ranges 

observed in the operating building

• Collecting and cleansing real-time weather data

• Running energy models to determine the 

acceptable hourly, daily, and annual end-use 

energy ranges

• Enabling model parameter optimization for cost 

and comfort that dynamically change ranges

The disadvantage in this scenario is clear; simulation 

setup is time intensive (and changes during the building 

life cycle). But, if the project contract requires an 

aggressive energy goal, an as-built energy model would 

likely exist; along with an architecture for data 

collection that supports dashboards and operational 

decision-making. The advantages are that the interface 

is dynamic enough to move toward automatic action 

taken on the building control system and predictive 

control for demand response cycles.

RESULTS

This section discusses the reduced-order model 

approach to end-use energy range predictions currently 

implemented in the BA, showing the development 

process, lessons learned, and positive outcomes of 

using a predictive interface in energy performance 

assurance in the RSF. 

Photovoltaic Panels

More than 2 megawatts of PV panels are installed on or 

near the RSF to offset the annual energy use to achieve 

net-zero energy. Renewable energy was the last 

consideration in the design process; however, after 

system efficiency measures, it is presented first because 

of the relative ease in determining a dashboard 

representation. 

An asset model is needed to predict energy use based 

on the final panel specification and configuration, but 

an operational model is unnecessary because there are 

no controlled components and the prediction is the 

energy production goal. Figure 5 shows that the 

continuous reduced-order model was determined by 

first plotting the modelled production against 

horizontal radiation, a known dominant state. 

Boundaries are then visually added and fitted with 

equations that are implemented in the BA’s application 

layer.

Figure 5: PV Production Versus Global Irradiance

The visualisation layer of the PV predicted range is 

shown in Figure 6.

Figure 6: PV Production Predicted Range 

Visualisation (May 15, 2013, partly cloudy day)

Lighting

The RSF occupants manually control electric lighting 

“on” in the RSF; it is automatically controlled “off” by 

daylight and occupancy sensors and timed sweeps. 

Daylighting is important in a net-zero energy office 

building, so a detailed daylighting model was produced 

in design, the results of which were compared to the 

first-year measured data as an energy performance 

assurance step.

Figure 7 shows the importance of an operational model. 

The nighttime lighting energy use is much higher than 

predicted in the design model because the system use 

by a custodial crew was not considered during design. 

y = -1,937.645688x2 + 3,953.554779x - 180.419580

y = -862.47086 x2 + 2,798.83450 x - 265.73427 

-500

0

500

1000

1500

2000

0 0.2 0.4 0.6 0.8 1 1.2

Total PV output for a all RSF/Garage PV (2.4MW)

Global Horizontal solar (kw/m2)

Pvwatts hourly data

Max output line

min output line

Poly. (Max output line)

Poly. (min output line)
6

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The annual average daytime lighting energy use

matches the model well; therefore, operational data are

used to determine the predicted energy ranges.

Figure 7: First-year Lighting Operation Hourly 

Average Power Profile

Figure 8 shows that the daytime lighting energy use 

depends largely on exterior vertical irradiance. This 

factor was selected based on experts’ judgments 

because, unlike PV, it was not clear which exterior 

radiation value is the dominant state. The process for 

determining the daytime predicted ranges then 

followed that for PV. The nighttime lighting load is 

dominated by occupant behaviour that is consistent 

from day to day, lending itself to a lookup table for the 

BA application layer. 

Figure 8: Lighting Power Density (W/m2

) Versus 

Exterior Vertical Irradiance (W/m2

) 

The lighting predicted ranges show a combined 

continuous reduced-order model and lookup table, as 

seen in the dashboard implementation in Figure 9.

Figure 9: Lighting Predicted Range Visualisation

Plug Loads

RSF plug loads are regulated; each occupant has a 55-

watt allowance for a laptop, monitors, phone, task light, 

and miscellaneous items. The energy model accounts 

for this load, but can only infer a diversity factor of 

occupant use throughout the day. 

The first step in operational energy performance 

assurance is to compare the first-year measured data, or 

initial operational model, to the design modelled data. 

Figure 10 shows that the hourly average daytime 

operational load is much lower than the hourly average 

design prediction, but the nighttime load is higher. 

Because the model suggests energy-saving potential, 

the lower of both datasets at any given hour is used to 

determine the dashboard energy goal. 

Figure 10: First-Year Plug Load Operation Hourly 

Average Power Profile

The high and low ranges were determined by visually 

bounding the first-year data range, excluding outliers, 

and implementing the range in the dashboard using a 

lookup table, because time of day is the most obvious 

factor in the primarily occupant-driven load. The 

resulting visualisation is shown in Figure 11.

Figure 11: Plug Loads Predicted Range Visualisation

(February 13, 2013)

Mechanical 

The RSF’s mechanical loads consist primarily of 

ventilation air fans and pumps for radiant heating and 

cooling fluid. The RSF has hot water and chilled water 

flow meters to account for its load on the central plant.

The performance assurance and predicted range 

evaluation for the mechanical systems mirror the 

process for the plug loads, with two exceptions:

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Power Density (W/ft2)

Time of Day

 

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Upper Bound

Measured Average

Lower Bound

0

0.05

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0.15

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0 20 40 60 80 100 120 140

 

 

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7

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First, the design model predictions overestimate energy 

use during the day instead of at night, so the first-year 

metered data are used as an upper bound at night and

the design model results are used as an upper bound 

during the day. This process is demonstrated in Figure 

12.

Figure 12: First-Year Mechanical Systems Operation 

Hourly Average Power Profile

Second, like the plug loads, a lookup table is used in 

place of a continuous reduced-order model, but this is 

not the best representation of the performance data,

because that hourly and annual variation is much 

greater than plug loads, which do follow a regular 

schedule. The mechanical system ranges would be 

better suited for a real-time energy model in which

many factors such as weather, control algorithm, and 

occupant input can create more dynamic predicted 

ranges for this end use. Figure 13 shows the interim 

result.

Figure 13: Mechanical Systems Predicted Range 

Visualisation (June 06, 2013)

DISCUSSION

The process used to determine the predicted range 

models for the RSF, the first building represented by 

the BA, is presented as an example of the energy 

assurance process emerging in the commercial 

buildings industry. The process consisted of design and 

metered data comparisons, the validation or 

development of an asset model for systems, and an 

operational model for control variables, where 

necessary. Then, a visually determined continuous 

reduced-order equation or lookup table was applied to 

the asset and operational models and data, respectively. 

For uncontrolled systems such as PV, visually 

bounding asset model outputs relative to known 

dominant variables to determine reduced-order 

equations is a simple and sufficient process. The 

information that occupants need to know about these 

types of systems is whether the system is working and 

whether it is degrading.

On the other end of the spectrum, complex controlled 

systems for fans and pumps need more rigorous 

approaches to determining predicted energy use ranges. 

The reduced-order lookup table approach used for the 

BA is not sufficient to capture the hourly and daily 

differences between potential and actual performance, 

leading to a missed opportunity for energy savings.

As the focus on energy performance assurance 

sharpens, the commercial buildings industry will 

develop more rigorous and automated approaches to 

determining predicted ranges and automatically 

refining those ranges based on real-time operational 

models. The RSF process can serve as an example and 

dataset for that effort.

Value in predicted range visualisation has been realized 

at NREL through anecdotes to date:

• The high plug load use at night relative to the 

predicted ranges has lead NREL energy 

engineers, information technology department, 

and managers to work together to communicate 

and give options to employees for turning off 

monitors, computers, and other miscellaneous 

loads at night. Best practices were developed 

based on the lessons learned (Lobato, Pless, 

Sheppy, et al. 2011).

• A high nighttime lighting load has led to training 

sessions, reminding custodial crews to turn of 

ambient lights when they leave an area and to 

use the egress switches when possible, which 

often provide sufficient light for the tasks being 

performed.

• A consistently high data centre load relative to 

the model gave the data centre manager 

evidence to request funding for improved hot 

aisle containment strategies.

• Last but not least, the whole-building energy 

display comparing the RSF energy use to the 

contractual EUI and net-zero energy goals has 

lead to more careful consideration during 

building upgrade projects. For example, the 

addition of a 24-hour visualisation room for 

security was not originally designed for isolated 

heating, cooling, or ventilation. Isolated systems 

were added once the building energy engineer 

was able to show that the building would tip past 

its annual operating goal if the building systems 

were used for this system, because the thermal 

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Power Density (W/ft2)

Time of Day

 

Model Average

Upper Bound

Measured Average

Lower Bound
8

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

zones are much larger than the centre being 

added.

The visualisations have provided information and the 

ranges have added the needed justification for building 

energy projects.

CONCLUSIONS

The RSF dashboard development case study reveals the 

importance of defining a complete architecture and 

infrastructure, such as BA, for a building-occupant 

interface. The architecture must be considered in early 

project planning to ensure that hardware such as 

submeters are in the purview of the design and 

construction team. Also, owners must account for the 

human and computing resources necessary to create asbuilt asset models and ongoing operational models. A 

final construction or asset model is a sufficient tool for 

representing some end uses, but an operational model is 

needed for the high energy-saving potential systems 

that are occupant or automatically controllable. 

The BA dashboard moved the RSF operational practice 

from presenting non-contextualized building 

performance to a first-order data analysis with 

boundary conditions (presenting end uses with 

predicted ranges of acceptable energy use). Future 

work will include efforts to:

• Automate “the art” of building expert-based 

generation of reduced-order model variable 

selection 

• Include occupant comfort boundaries as well as 

energy boundaries

• Develop and implement fully automated models 

with agent reasoning, and proactive comfort 

maintenance goals and energy achievement 

goals

• Integrate BA architecture definition into the predesign phase energy assurance process

The ultimate goal of the BA interface is a tool that 

extends the concept of integrated design to integrated 

operations, accounting for many operational factors 

such as energy price structures and varying occupant 

preferences. 

ACKNOWLEDGMENT

The authors wish to thank NREL sponsors including 

Ben Kroposki, Stuart Macmillan, and Mark Ruth. In 

addition, we would like to thank Chad Lobato for his 

contributions to the initial energy performance 

assurance activities for the RSF, Gregor Henze of the 

University of Colorado for his continued support of 

automated model selection, and the entire Building 

Agent Team including Kyle Benne, Larry Brackney, 

Katherine Fleming, John Krueger, Nick Muerdter, 

Anya Petersen, and Alex Swindler. We would also like

to extend a general acknowledgment to the many 

NREL campus integrated design and construction team 

members for their contributions to the NREL building 

procurement and performance assurance process and 

BA architecture.

REFERENCES

DOE. (2010). 2010 Buildings Energy Data Book. 

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Leach, M.; Lobato, C.; Hirsch, A.; Pless, S.; Torcellini, 

P. (2010). Technical Support Document: Strategies 

for 50% Energy Savings in Large Office 

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49213.

http://www.nrel.gov/docs/fy10osti/49213.pdf

Lobato, C.; Pless, S.; Sheppy, M.; Torcellini, P. (2011). 

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