NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

The Role of Modeling When 

Designing for Absolute Energy 

Use Intensity Requirements in a 

Design-Build Framework 

Preprint 

Adam Hirsch, Shanti Pless, Rob Guglielmetti, 

and Paul A. Torcellini

National Renewable Energy Laboratory

David Okada and Porus Antia

Stantec

Presented at the ASHRAE Winter Conference

Las Vegas, Nevada

January 29 – February 2, 2011

Conference Paper

NREL/CP-5500-49067

March 2011
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Adam Hirsch, Shanti Pless, Rob Guglielmetti, and Paul Torcellini are engineers in the commercial building group at the National 

Renewable Energy Laboratory, Golden, CO. David Okada and Porus Antia are engineers at Stantec Engineering, San Francisco, CA. 

1 

The Role of Modeling When Designing for 

Absolute Energy Use Intensity 

Requirements in a Design-Build Framework

Adam Hirsch David Okada Shanti D. Pless Porus Antia

Ph.D., ASHRAE ASHRAE, PE ASHRAE ASHRAE 

Rob Guglielmetti Paul A. Torcellini

IESNA, LEED AP Ph.D., PE

ABSTRACT

The Research Support Facility at the National Renewable Energy Laboratory is a 220,000-ft2 office building designed to 

serve 822 occupants, to use 35.1 kBtu/(ft2

·yr), to use half the energy of an equivalent minimally code-compliant building, and 

eventually to produce as much renewable energy annually as it consumes. These goals and their substantiation through 

simulation were explicitly included in the fixed price design-build contract. The energy model had to be repeatedly updated 

to match design documents and the final building, as it was built, to the greatest degree practical. Computer modeling played 

a key role in diagnosing the energy impacts of program and decisions and in verifying that the contractual energy goals 

would be met within the specified budget. The primary tool used was a whole-building energy simulation program. Other 

simulation tools were used to provide more detail or to complement the primary tool as required by the delivery schedule, 

including tools to calculate thermal bridging, daylighting, natural ventilation, data center energy consumption, transpired 

solar collectors, thermal storage in the crawlspace, and electricity generation by photovoltaic panels. Results were either fed 

back into the main whole-building energy simulation tool or used to post-process model output to provide the most accurate

annual simulations possible. This paper details the models used in the design process and how they informed important 

program and design decisions from design to completion. 

INTRODUCTION

The National Renewable Energy Laboratory’s (NREL) mission is to advance the U.S. Department of Energy’s and the 

nation’s goals in the areas of energy security, environmental quality, and economic vitality. From the beginning, NREL

recognized that its new Research Support Facility (RSF) represented a unique opportunity to demonstrate the state-of-the-art 

in energy-efficient, cost-effective commercial office design, construction, and operation. Today, buildings use roughly 40% 

of total primary U.S. energy consumption (22% residential, 18% non-residential); energy consumption in this sector is 

projected to grow by 25% in the next two decades1

 1 http://buildingsdatabook.eren.doe.gov/docs/xls_pdf/1.1.3.pdf

. The RSF is intended to prove that significant gains in energy efficiency 

can be realized cost effectively in commercial buildings with available technologies if careful attention is paid to project 

energy goals, the delivery process, and integrated design. We examine the details of how computer simulation tools were 
2 

used to help design the RSF and what capabilities the project required of those tools. We also present a portrait of how setting 

an absolute whole-building energy consumption target changes the role of energy modeling. Several teams were involved in 

the competition phase; however, we focus on the activities of the design-build team that was awarded the contract.

PROJECT OVERVIEW

The RSF project contains several novel features related to the delivery model, team structure, and Request for Proposals 

(RFP). Descriptions of how energy modeling was used in the design process follow. 

Delivery Model

NREL decided early that to deliver the RSF, with its challenging performance requirements, on time and on budget, a

traditional design-bid-build procurement process would not suffice. Rather, a performance-based “Best Value DesignBuild/Fixed Price with Award Fee” delivery approach (Post 2010) was pursued to encourage innovation, reduce risk, 

expedite construction and delivery, control costs, make optimal use of team members’ expertise, and establish measurable 

success criteria. The RSF procurement strategy provides an important context for understanding the design process and the 

use of energy modeling tools. By hiring a design-build team, NREL encouraged an integrated design process comprising 

architects, engineers, and builders working toward well-defined goals. This arrangement resulted in an iterative design 

pattern involving the entire team. Detailed computer simulations were used to assess whether the design as it evolved would 

meet performance requirements. It also required NREL to clearly define the scope and goals in the RFP and then allow the 

design-build team to find creative solutions. Having specific end-use and whole-building energy use goals necessitated that 

energy modeling be included in the design process from the beginning. The fixed budget for all work (conceptual design, 

preliminary design, final design, and construction) of $64 million, formulated by the U.S. Department of Energy, was 

determined before energy goals were established. This fixed budget, coupled with the energy goals, required that cost 

modeling be emphasized as much as energy modeling. The selected design-build team performed a great deal of conceptual 

phase energy modeling in the months leading to the submission of its design competition proposal in March 2008. 

Preliminary design occurred from July 2008, when the contract was awarded, through November 2008. Final design took 

place from January 2009 through July 2009. Construction began in February 2009 and lasted until June 2010, when it was 

turned over and ready for occupancy. 

Objectives

In the conceptual documents of the original RFP (dated February 6, 2008), the objectives were prioritized and then 

divided into three groups: Mission Critical, Highly Desirable, and If Possible. Competing design-build teams were judged 

based on their ability to meet as many objectives as possible while meeting the overall budget constraint. A subset of those 

goals related directly to energy modeling and the low energy design process includes: 

1. Mission Critical

a. LEEDTM Platinum designation 

b. ENERGY STAR® appliances, unless another system outperforms

2. Highly Desirable

a. 800 staff capacity

b. 35.1 kBtu/(ft2

·yr)2

c. Measurable 50% energy savings versus ASHRAE 90.1-2004

3. If Possible

 2 Absolute energy goals of 25, 32, and 35.1 kBtu/(ft2

·yr) all appear at certain points in the project documents; 25 kBtu/(ft2·yr) assumes 650 occupants and 

data center energy use prorated to reflect that only a portion of the data center services are consumed in the RSF; 32 kBtu/(ft2

·yr) assumes 822 occupants 

and prorated data center energy; 35.1 kBtu/(ft2

·yr) includes the entire data center energy consumption.
3 

a. Net zero energy building approach

b. LEED Platinum Plus

c. Exceed 50% savings over ASHRAE 90.1-2004 baseline

The absolute site energy consumption, net zero energy balance, and LEED Platinum goals in particular influenced the 

modeling tools and design process. The RFP also specified parameters such as temperature and humidity set points, nighttime

setback, maximum U-value of windows, and compliance with elements of ASHRAE 55 that have energy impacts. 

Energy Goal Setting

 The absolute energy consumption target was chosen based on analysis done as part of a sector-wide energy efficiency 

modeling study (Griffith et al. 2006). As part of that study, energy models of all the buildings in the 2003 Commercial 

Building Energy Consumption Survey were created using EnergyPlus. These models were then modified to make them 

minimally compliant with ANSI/ASHRAE/IESNA Standard 90.1-2004 requirements. Tables containing these results are 

available online3

MODELING IN CONCEPTUAL AND PRELIMINARY DESIGN PHASES

for the industry at large to facilitate energy goal setting. An absolute energy use intensity goal was chosen to 

correspond to half the average energy use of the simulated office/professional buildings in ASHRAE climate zone 5B, which 

includes Golden, Colorado. This goal was in line with the measured performance of several high-performance office 

buildings analyzed in detail by the Commercial Buildings group at NREL (Torcellini et al. 2006). Further feasibility studies 

carried out in 2007 indicated that this target should be achievable for an office building in the Denver area if special attention 

were paid to optimizing glazing area to maximize daylighting yet minimize thermal losses; emphasizing the building’s eastwest axis to facilitate the daylighting goal (daylight is easiest to control and harness from the north and south); and 

minimizing plug and process loads. The modeling study used NREL-developed design optimization software to explore 

design options that could lower end-use energy consumption. The optimization modeling begins with an initial baseline 

whole-building energy model then systematically alters the design features, at each stage selecting the most cost-effective 

energy saving design options. 

The project team engineers conducted early feasibility studies to assess whether the absolute energy goal set in the RFP 

was realistic and how the form of the building needed to reflect the energy goals. Even before the internal design-build 

charettes began, initial simulations showed that daylighting and natural ventilation would be critical for success. The building 

form quickly came to reflect these strategies, with two 60’ wide (north-south dimension) office wings, connected by a central 

core of conference spaces in the shape of a “lazy H,” spaced to minimize self-shading. At this early stage of conceptual 

design, self-shading was studied using a 3-D modeling software package; energy modeling was performed with a wholebuilding energy analysis tool. Hydronic radiant heating and cooling in the ceiling slab and daylighting (with continuous 

dimming lighting controls) were included in these runs. Numerous design features were not included in the proposal concept 

energy model, but were incorporated in subsequent design phases, such as winter ventilation preheating (using a double skin 

façade design), summer, occupancy sensors, natural ventilation, daylight redirection, and demand control ventilation. The 

radiant heating and cooling system is modeled in the whole-building analysis as a fan coil unit with nearly zero fan energy; 

however, the comfort and control implications of the radiant slab conditioning were still carefully considered.

Early concept design drawings for the winning proposal showed a double skin façade. This approach was later 

eliminated based on multi-disciplinary design coordination, thermal analysis (using a second whole-building analysis 

package with more detailed thermal modeling capabilities), and cost modeling in favor of an approach using a single façade 

and transpired solar collectors. Producing timely energy modeling projections was a constant challenge for the design-build 

team because of the compressed design and construction schedule. Rather than relying solely on one tool, the team used a 

constellation of models to provide detailed analysis of building components (Figure 1). The results were then either fed back 

 3 http://apps1.eere.energy.gov/buildings/publications/pdfs/commercial_initiative/energy_use_intensity_targets.pdf
4 

into the main whole-building energy model or used to post-process its output to provide an integrated picture of wholebuilding energy use. For example:

• Daylighting calculations were performed with detailed lighting simulation tools, including window shading and light 

redirection; the daylight performance and correlated electric lighting dimming and switching responses were fed into 

the whole-building energy model; 

• Thermal effects of natural ventilation strategies were analyzed using a separate whole-building energy modeling tool 

with more advanced thermal analysis capabilities and used to post-process the results of the main energy analysis 

model; 

• Data center energy use, including electricity consumed by information technologies equipment, cooling energy, fan 

energy, and heat recovery, was modeled outside of the main energy model and added to its energy projections; 

• A separate model of air heating by transpired solar collectors on the south face of the building was used to postprocess the energy modeling results; 

• Ventilation preheating benefits of the crawlspace during the heating season, with inputs from the data center and 

transpired solar collector models, were modeled using a separate finite-difference thermal model and combined with 

the energy model output; 

• Renewable energy generation by PV panels was modeled separately. 

Figure 1 Modeling information flows

More details of each module in the energy modeling suite follow. 
5 

Daylighting/Lighting Modeling

A lighting simulation tool was used to quantify annual daylighting characteristics and to establish optimal photosensor 

placement for energy savings. It is driven by a physically based, backward ray-tracing simulation designed to provide 

accurate quantitative and qualitative daylight and electric lighting predictions, even when considering complex fenestration 

systems such as daylight redirection devices. The lighting tool was initially employed in the design competition phase in the 

context of the LEED daylight credit iEQ8.1, mandated in the RFP. Analysis of the workplane illuminance under clear skies at 

noon on the equinox – a metric for assessing compliance with this criteria – showed that even with best practices of 

separating view glass and daylight glass and using a daylight redirection device to reflect incoming sunlight deeper into the 

building, a maximum floor depth of 60’ could be daylit sufficiently to satisfy the requirements. This consideration effectively 

set the building’s footprint. For the energy modeling, the lighting analysis tool was able to provide an 8,760 hour schedule of 

the lighting power fraction for an electric lighting system with dimming and switching controls to match the specified office 

illuminance set point. This schedule can be passed seamlessly to the whole-building energy model. For this analysis, the 

typical office space was divided into a south perimeter zone, a core zone, and a north perimeter zone. Other daylit spaces 

were simulated using the built-in continuous dimming sensor and daylighting calculations available in the energy model. 

Installed lighting power density in open office areas is very low – only 0.62 W/ft2

, facilitated by the open office structure (no 

obstructions), highly reflective room surface finishes, ability to use a regular grid, and efficient electrical lights. 

Natural Ventilation Modeling

Window configurations and control strategies for natural ventilation were analyzed by modeling the building in a 

separate whole-building energy analysis tool to enable explicit modeling of the thermal effects of natural ventilation and 

thermal mass effects, which the main energy modeling software cannot do. The natural ventilation modeling was performed 

without including mechanical systems, so results represent passive conditioning. Then, on days when the as-designed 

window arrangement and natural ventilation controls yield temperatures that do not exceed 78°F or fall below 69oF, the 

thermostat cooling set point was set to 100oF (turned off) in the main office wing spaces. Preliminary modeling showed that 

night purging to cool the office spaces would be a promising strategy to save energy. Natural ventilation modeling made an 

impact on the preliminary building design in the form of low-level motorized windows being added to enhance stack effect 

ventilation and allow better night cooling.

Data Center Cooling and Heat Recovery Calculations

Data center equipment was modeled as a continuous 24/7 load of 65 W per person (this does not include energy 

consumption by cooling equipment), based on a survey of typical data center configurations. The owner selected temperature 

and humidity criteria consistent with the 2008 ASHRAE Environmental Guidelines for Datacom Equipment (ASHRAE, 

2008). Complete hot and cold aisle separation was assumed. The cooling system was simulated with and without airside 

economizers, with appropriate adjustments made to the fan system pressure drops. The TMY3 climate data were analyzed to 

understand the psychrometric processes that could be used to achieve the required supply air temperatures. We found that an 

economizer and direct evaporative cooling could be used to cool the data center for 99.5% of hours with no mechanical 

cooling, partly because of its assumed broad acceptable supply dry bulb temperature range (66.4-77oF), as recommended by 

the ASHRAE Datacom guidelines. At this stage, the economizer and direct evaporative cooling strategies were predicted to 

save substantial energy, but were not included in the actual design because the incremental capital cost was estimated to be 

too high. Eventually, these strategies were included in the design, with the ability to cool mechanically for the limited number 

of hours when analysis of TMY2 data suggests dehumidification will be required. Energy savings by the servers were not 

included; this is a conservative assumption, as the NREL Information Technologies department will probably use equipment 

that is more efficient than the industry standard.

The heat from the data center available for recovery was modeled for several scenarios. First, an “economizer” case was 

modeled assuming direct discharge of the hot aisle air into the crawlspace, where it would warm the mass during non-
6 

occupied hours and be used during occupied hours to preheat ventilation air. A “no-economizer” case was also modeled 

employing a runaround loop to transfer heat from the data center hot aisle to preheat domestic hot water and ventilation air. 

More efficient data center equipment would reduce the amount of heat available for ventilation preheating; however, future 

efficiency gains were assumed to be matched by increased data center us,e so the overall load would remain the same. 

Transpired Solar Collector Air Heating Calculations

Delivery of hot air from the transpired solar collectors to the crawlspace during the heating season was modeled 

separately using manufacturer data, making simple assumptions about airflow through the transpired solar collectors, an 

appropriate value of cfm/ft2

, absorptivity of the collector, fan efficiency, and fan pressure drop. Ambient air temperatures and 

solar radiation (direct and diffuse) were taken from TMY3 weather data, with appropriate adjustments made based on sun 

angle relative to the collector surface. An 8,760 hour schedule of outlet temperatures was passed along to the crawlspace

thermal calculations.

Crawlspace Energy Calculations

The first floor of the RSF had to be constructed above grade because the site has expansive soils. Early in the design

process the team conceived that the below-grade crawlspace formed by the foundational piers could be used to precondition 

ventilation air – storing heat from the transpired collectors and data center and delivering it when needed in the heating 

season and removing heat in the cooling season after nighttime precooling (Torcellini et al. 2010). This crawlspace is referred 

to as a remote-mass labyrinth because of its maze-like appearance. To model energy savings, it was divided into sections 

defined by the piers and finite difference heat transfer nodes defined to represent the slab perimeter, slab center, perimeter 

walls, and the grade beam. Nodes representing the earth below the slab and outside the perimeter walls were also included, 

and in turn were connected to boundary conditions of the undisturbed ground surface temperature for the perimeter and 

constant deep ground temperature below the slab. Air nodes were included to complete the energy balance. The energy flows 

were then calculated using a spreadsheet program for 8,760 hours of the year to characterize the heat exchange between 

concrete and air (neglecting the heat capacitance of the air nodes). The separate data center and transpired collector 

calculations provide a schedule of available temperature and flow rate from those two heat sources. The ventilation system is 

designed to deliver space-neutral air, so the flow rate from the data center and transpired collector and the mixing with 

outdoor air can be controlled to deliver 68oF air at the volume needed to meet ventilation requirements. 

MODEL EVOLUTION AND INCREASING DETAIL IN SUBSEQUENT DESIGN PHASES

The main whole-building energy model developed in the preliminary design phase contains 247 zones to account for 

the loads in every typical space. This level of modeling is indicative of the attention to detail required by the absolute energy 

goals. It quickly became clear- that accurate assumptions about the data center energy use and miscellaneous plug and 

process loads would be at least as important as the thermal modeling. Early modeling substantiation showed server electricity 

use representing one-third of the building’s total annual energy consumption and miscellaneous plug loads another 23%. 

Energy use by office and exercise equipment, audiovisual equipment in the telepresence room, the security system, elevators, 

the fire alarm system, and even a coffee cart was carefully tabulated and included. Eventually exit lighting, sump pump 

energy, and parasitic losses in the lighting panels would all be incorporated. Occupancy and equipment schedules were 

repeatedly updated to reflect owner modifications. The eventual plug load density, including a constant 0.35 W/ft2 demand by 

the data center, was modeled to be 0.85 W/ft2 during occupied hours and 0.54 W/ft2 during unoccupied hours (Lobato et al.

2010). 

Lighting, Daylighting, and Natural Ventilation

During the core and shell design development, a limitation was discovered in the number of input files that could be 

taken from the daylighting simulation and incorporated into the whole-building energy model. Although 22 files were 
7 

developed to modify lighting schedules for typical RSF office spaces, only 4 could be added before the energy model code 

reached its maximum length. As a workaround, lighting files representing south perimeter and core spaces in both wings 

were used to model all those spaces, and the energy model’s built-in daylighting controls were used for the north perimeter 

spaces in both wings. Layout and fixture changes in the electrical lighting design were incorporated into the energy model as

they occurred. For the final round of energy modeling, seven lighting files were incorporated into the model as other 

schedules were combined.

Natural ventilation modeling analysis of the fitness center, connector wing, and information commons (library) was also 

performed in the core and shell design development, implementing the lessons learned in the preliminary modeling. The 

effectiveness of night purging was analyzed for different window arrangements to identify the best configuration of manual 

and automatic windows and their control for effective cooling. We found that natural ventilation would likely be ineffective 

in the connector wing, mainly because of high transient internal loads (for example, densely occupied conference rooms) 

relative to the amount of perimeter façade available for ventilation. Also, the full exterior building geometry (rather than that 

of a typical wing) was eventually included in the natural ventilation model to more realistically capture shading. The 

technique for quantifying the cooling savings for natural ventilation was changed from adjusting the thermostat set point on 

select days to post-processing cooling energy results on select days. The design team also tried to capture the influence of 

occupant behavior vis-a-vis window operation by halving the potential energy savings from natural ventilation to reflect the 

possibility that it would be suboptimal.

Thermal Bridges 

In the same way small details have a large impact on simulated energy use, small architectural details can affect the 

energy use bottom line by compromising the envelope’s thermal properties. Two-dimensional heat transfer analysis and fin 

heat transfer formulas were used to study thermal bridging in the balconies at the east and west ends of the RSF. A detailed 2-

D heat transfer model was used to derate the U-values of the constructions used in the energy model for the whole-building

energy model, to investigate design strategies to minimize the bridging, and identify likely areas of problematic 

condensation. Areas initially studied included the connection of the superstructure to the substructure and balcony attachment 

points. In all cases the design-build team adopted mitigating measures. Later this analysis was used to evaluate the insulation 

required between the radiant ceiling slabs and the adjacent supply air floor plenums and prove the necessity of avoiding a 

specific standard detail for mounting photovoltaics (PV) to the roof. This drove a complete change of the roofing system to a 

standing seam strategy to avoid penetrations. The standing seam structure was affixed to the roof membrane; the funds came 

from the project contingency.

Photovoltaic Panel Electricity Generation 

A software tool originally developed at NREL was used to size and calculate expected PV output to match the demandside energy goal. The modeling of PV to match the demand side pushed the design team to maximize the amount of roof area 

available for PV, ensure the 14 degrees of south orientation for the north wing did not significantly degrade the optimal 

output, and optimize the roof tilt to balance the need to maximize output with the additional cost of building structure at the 

optimal tilt. A 10-degree roof tilt angle was determined to provide an optimal balance between PV output degradation and

additional roof and building costs for the tilt. A total of 1.7 MW of PV will eventually be required to offset annual energy 

consumption.

Baseline Simulation

The project RFP included an absolute energy use intensity target for energy consumption, a net zero energy balance 

target, and a minimum 50% energy savings versus a minimally code-compliant equivalent building with the same 

unregulated loads and program. This requirement necessitated construction of a baseline building model, built according to 

the requirements laid out in Appendix G of ASHRAE 90.1-2004, which include distributing glazing in equal bands around 
8 

the building, eliminating shading devices, modeling a standard HVAC system type with airside economizers and screw 

chillers with a coefficient of performance of 4.9, and using standard lighting power densities per space type. U-values of the 

opaque envelope and glazing assemblies were taken from ASHRAE 90.1-2004 Table 5.5-5. In the baseline case, computer 

workstations were assumed to consume 120 Watts versus the 65 Watts in the low energy case. 

Closeout Modeling

As construction neared completion, the energy model was again updated to reflect minor modifications and new 

information available from the construction. Changes include pump and fan power as balanced, revised assumptions for plug 

load equipment, and in some cases measured parasitic power draws of specific components.

LESSONS LEARNED

One of the most important lessons of the RSF modeling effort is the degree to which unregulated plug and process loads 

can dominate energy use, and the degree to which these loads must be understood and controlled to meet a stringent absolute 

whole-building energy use target. Energy modeling typically focuses on envelope properties, mechanical systems, and 

regulated internal loads, but for the RSF great attention was paid to details such as schedules and the energy use of individual 

items ranging from telephones and task lights to lighting control systems (a constant 728 Watts in the case of the latter), 

distribution transformer losses, white noise generators, and sump pumps. Architectural details such as thermal bridging in 

window mullions (which might typically be ignored) were also important considerations. When detailed calculations of 

window performance were done instead of taking overall U-values from ASHRAE Fundamentals (Chapter 15 table 4), 

expected U-values were found to be 30% greater. 

With a whole-building absolute energy use goal, plug and process loads cannot be assumed to be the same between 

baseline and low-energy models and therefore “fall out” of the analysis when the two models are compared. They must be 

carefully modeled before construction and measured when the building is occupied. A corollary to this lesson is that the 

building owner, who controls the building program, must be committed to the energy goals and monitor end use energy 

consumption over time to make building performance match design and to sustain those energy savings. A design team 

cannot deliver a building that automatically reaches aggressive whole-building energy use targets; the building must emerge 

from an integrated design team that places energy use intensity at the top of the checklist throughout and uses rigorous 

simulation to verify the design’s performance at every milestone.

The energy modeling process required close coordination between the engineering and design teams because of the 

expedited design-build process and the RFP’s strict substantiation requirements. There was a constant tension between the 

model detail required for accuracy and the compressed design-build schedule. In retrospect, the energy modeling process 

should have been included as significant constraint instead of being squeezed into a schedule that was based primarily on 

design documentation and construction. This would likely have resulted in fewer design changes, a more streamlined 

decision making process, and a more integrated building. The workaround calculations, such as the heat budget of the 

labyrinth, were critical to the energy modeling process, but were significantly more time consuming than originally 

anticipated. When approaching these types of calculations in the future, the team would be much more likely to model these 

in an existing energy model with detailed heat transfer simulation capabilities rather than completely from scratch. Modeling 

uncertainties led the team to build an energy-use contingency into its design to provide a cushion should some assumptions 

fail. The RSF closeout energy modeling report cites a final energy use of 33.3 kBtu/(ft2

·yr), 5.9% better than NREL’s goal of 

35.1 kBtu/(ft2

·yr). 

Energy modeling played a dramatically different role in this project versus a project where energy modeling is required 

only for code compliance or voluntary certification programs. The team had to model early and often at a high level of detail, 

to gain confidence that the building as constructed would meet its energy use goals. Energy modeling fundamentally 

influenced the design of the building, including the narrow floor plate; the size and performance of windows; architectural 

detailing to avoid thermal bridging; the data center cooling approach; and office equipment.
9 

CONCLUSION

Energy modeling helped the RSF design-build team to assess from the outset whether the design would be able to 

accomplish NREL’s ambitious energy efficiency goals. The modeling process pushed the team’s available energy modeling 

toolkit to, and perhaps past, the limit of its capabilities. Separate models were required for daylighting, natural ventilation, 

data center energy use, crawlspace heat recovery and storage, and renewable energy generation. The team successfully knit 

these outputs together; ultimately, however, a tool that could simulate the RSF from start to finish more seamlessly and with 

a reasonable learning curve would have been advantageous. Detailed monitoring of the RSF operational energy use has 

begun and the building is now partially occupied. These data will be used to verify model assumptions, calibrate a more 

detailed EnergyPlus model, and quantify operational energy use across days, seasons, and years.

REFERENCES

ASHRAE, 2008. 2008 ASHRAE Enironmental Guidelines for Datacom Equipment – Expanding the Recommended 

Environmental Envelope. 11 pp. American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc., 

Atlanta,GA. http://tc99.ashraetcs.org/documents/ASHRAE_Extended_Environmental_Envelope_Final_Aug_1_2008.pdf

Griffith, B., Torcellini, P., Long, N., Crawley, D., and J. Ryan. 2006. Assessment of the Technical Potential for Achieving 

Zero-Energy Commercial Buildings: Preprint. 15 pp. NREL Report No. CP-550-39830.

Lobato, C., Pless, S., Sheppy, M., and P. Torcellini. 2010. Reducing Plug and Process Loads for a Large Scale Ultra Efficient 

Office Building: NREL’s Research Support Facility. 8 pp. NREL Report No. CP-550-49002.

Post, N. 2010. Green Design-Build Model Crafted for Buildings To Achieve Net-Zero Energy Use. 

http://enr.ecnext.com/coms2/article_busu100505NetZeroEnerg-1 

Torcellini, P., Pless, S., Deru, M., Griffith, B., Long, N., and R. Judkoff. 2006. Lessons Learned from Case Studies of Six 

High-Performance Buildings. 151 pp. NREL Report No. TP-550-37542.

Torcellini, P., Pless, S., Lobato, C., and T. Hootman. 2010. Main Street Zero Energy Buildings: The Zero Energy Method in 

Concept and Practice: Preprint. 12 pp. NREL Report No. CP-550-47870.
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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

March 2011

2. REPORT TYPE

Conference Paper

3. DATES COVERED (From - To)

 

4. TITLE AND SUBTITLE

Role of Computer Simulation in Designing an Energy Efficient 

Building: Preprint

5a. CONTRACT NUMBER

DE-AC36-08GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

A. Hirsch, D. Okada, S. Pless, and P. Antia

5d. PROJECT NUMBER

NREL/CP-5500-49067

5e. TASK NUMBER

BEC71123

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

8. PERFORMING ORGANIZATION

REPORT NUMBER

NREL/CP-5500-49067

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

NREL

11. SPONSORING/MONITORING 

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfield, VA 22161

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

The Research Support Facility was designed to use half the energy of an equivalent minimally code-compliant 

building, and to produce as much renewable energy as it consumes on an annual basis. These energy goals and 

their substantiation through simulation were explicitly included in the project’s fixed firm price design-build contract. 

The energy model had to be continuously updated during the design process and to match the final building as-built 

to the greatest degree possible. Computer modeling played a key role throughout the design process and in verifying 

that the contractual energy goals would be met within the specified budget. The main tool was a whole building 

energy simulation program. Other models were used to provide more detail or to complement the whole building 

simulation tool. Results from these specialized models were fed back into the main whole building simulation tool to 

provide the most accurate possible inputs for annual simulations. This paper will detail the models used in the design 

process and how they informed important program and design decisions on the path from preliminary design to the 

completed building.

15. SUBJECT TERMS

research support facility; rsf; computer simulation; energy simulation program

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL

18. NUMBER 

OF PAGES

19a. NAME OF RESPONSIBLE PERSON

a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

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