Conference Paper

NREL/CP-550-48264

August 2010

Commercial Building Design 

Pathways Using Optimization 

Analysis

Preprint

Nicholas Long, Adam Hirsch, Chad Lobato, and 

Daniel Macumber

National Renewable Energy Laboratory

Presented at ACEEE Summer Study

Pacific Grove, California

August 15–20, 2010
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Commercial Building Design Pathways Using Optimization Analysis1

Nicholas Long, Adam Hirsch, Chad Lobato, Daniel Macumber 

National Renewable Energy Laboratory 

ABSTRACT

Whole-building simulation and analysis has demonstrated a significant energy savings 

potential in a wide variety of design projects. Commercial building design, however,

traditionally integrates simulation and modeling analyses too late in the design process to make a 

substantial impact on energy use. The National Renewable Energy Laboratory (NREL) 

commercial building group created an optimization platform called Opt-E-Plus that uses 

multivariate and multi-objective optimization theory to navigate a large parameter space and find 

economically valid, energy-saving solutions. 

The analysis results provide designers and engineers valuable information that influences 

the design. The pathways are not full “construction ready” design alternatives; rather, they offer 

guidance about performance and cost criteria to reach a range of energy and economic goals. 

Having this knowledge early in the design phase helps designers establish project goals and 

direct the design pathway before they make important decisions. Opt-E-Plus has been deployed 

on several projects, including a retrofit mixed-use building, a new NREL office building, and 

several nationwide design guides. Each of these projects had different design criteria, goals, and 

audiences. In each case the analysis results provided pathways that helped inform the design 

process. 

This paper will discuss the high-level optimization framework, describe how the analysis 

was deployed on three real projects to help inform various design pathways, briefly discuss the 

limitations of the current implementation, and quickly address the future development needed on 

the optimization framework to further integrate the analyses into the design process.

Introduction

Commercial building design traditionally does not use simulations and modeling to 

influence design until too late in the process. The National Renewable Energy Laboratory 

(NREL) has developed an analysis package designed for buildings specific applications, called 

Opt-E-Plus (NREL 2010), to help analyze commercial building energy use and consumption, and 

to influence building design early in the design process. Opt-E-Plus was applied to three 

separate design projects: a mixed-use retrofit project in Sacramento, a new net-zero energy 

Research Support Facility (RSF) at NREL, and a series of nationwide design guides. Each 

project had unique building types, owners, objectives, and audiences, but all three were 

committed to saving energy and reduce costs, and needed early design stage information to 

influence the overall building design. The analysis platform will be discussed at a high level to 

 

1 The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC (Alliance), a contractor of 

the US Government under Contract No. DE-AC36-08-GO28308. Accordingly, the US Government and Alliance retain a nonexclusive royaltyfree license to publish or reproduce the published form of this contribution, or allow others to do so, for US Government purposes.

1
provide a basic understanding of its functionality, and then the use and outcome of the platform 

results will be presented for each project. 

Problem Statement

Building simulation is not commonly integrated into the building design process in order to 

make meaningful changes to the building design. Common uses of building simulation are to 

determine code compliance and to achieve accreditation. Unfortunately, these activities typically 

occur too late in the process to influence the building design and so may not actually save energy 

(Turner & Frankel 2008). Furthermore, the standard code-compliant building is not the most 

energy efficient, but is the least energy-efficient building allowable. 

In order to allow modeling and simulation to impact the built environment, it is critical to 

create simulation models quickly, allow for high-level large-scale perturbations, verify the 

models and results, evaluate economic impacts, and present results that enable users to set goals 

and understand the relative energy and economic tradeoffs for their projects. NREL continues to 

overcome these difficulties by developing analysis tools that can be used to influence the design 

process during different stages. 

Opt-E-Plus in the Design Process

Opt-E-Plus was developed to support development of low- and net-zero energy buildings 

by integrating simulation and optimization in the design process. Energy and economic goals are 

still vague at the early design phase, so Opt-E-Plus presents a range of design options, each of 

which minimizes energy use at a particular economic cost. This range of design options is also 

known as a Pareto optimal front in formal multivariate optimization terminology (Ellis et al. 

2006). These options enable designers and engineers to set project goals based on a reasonable 

understanding of the tradeoffs between energy use and economics for a particular project. Such 

goals are more likely to influence the design process as they cannot be brushed aside as easily as 

goals with no quantitative backing. Opt-E-Plus utilizes the U.S. Department of Energy’s wholebuilding energy simulation engine EnergyPlus (Crawley et al. 2008) to ensure that interactions 

between energy design measures (EDMs) (e.g., lower lighting power density results in lower 

cooling energy but increased heating energy) are accurately captured. An EDM is a perturbation 

to the building model that influences the objective functions (it does not have to save energy). 

Although EnergyPlus is a very detailed calculation engine, the focus at this stage is on wholebuilding integration strategies rather than on details of a single subcomponent. 

How It Works 

The starting point for an Opt-E-Plus design optimization is a baseline model that 

encapsulates the basic requirements for the project, including location, floor area, program, 

loads, and other typical code-compliant options (HVAC, schedules, etc). After manually 

verifying the baseline’s input values, energy use, and economic results, the design team

generates a list of potential EDMs to be applied to the baseline model. The Opt-E-Plus internal 

building model maintains the logic needed for valid EnergyPlus files (e.g., applying an 

evaporative precooler will automatically place the component as the first component in the 

outside air stream). GenOpt (LBNL 2010) is a similar program that operates more generically 

allowing for multiple optimization algorithms on any simulation input file. Opt-E-Plus has 

2
similar functionality as GenOpt, except that it operates specifically on EnergyPlus’ input and 

output. 

Through automated processes, each EDM is applied to the baseline building to create 

candidate models. The costs of the EDMs are typically dimensioned to account for changes 

during the optimization such as costing the HVAC per cooling capacity or walls per area. Each 

candidate model falls into four possible quadrants and the optimal candidates bounded by the 

Pareto optimal front (curved line) as seen in Figure 1. The X-axis in the Figure 1 is Energy 

Savings; however, depending on the design objective, it may be beneficial to display the units in 

absolute energy (e.g., design goal for net-zero energy building), in which case the curve will be 

in the opposite direction. 

Figure 1: Four Quadrants of Simulation Results

Each EDM must represent a design option that the developer believes can be practically 

applied. Each EDM comes with associated costs that represent a best estimate of the incremental 

cost to apply it over the baseline case. More constraints (such as number of stories) can be added 

to the baseline definition at this stage, but these will limit the EDM search space explored and 

may reduce opportunities for energy and cost savings. 

Once the baseline model and EDMs are defined, Opt-E-Plus employs an iterative search 

technique to find EDM combinations that best balance percent energy savings with life cycle 

cost when applied to the baseline building (Ellis et al. 2006). At each iteration, Opt-E-Plus 

applies the possible EDMs to the current Pareto optimal building design and performs an 

EnergyPlus simulation for each combination. If any newly generated building design is chosen 

as a new Pareto optimal design, the iterative algorithm continues. This algorithm greatly reduces 

the number of potential building designs evaluated relative to a full enumeration of all possible 

options. Opt-E-Plus can take advantage of supercomputer resources at NREL to run these 

simulations in parallel. In this way, a fully parameterized space of more than a million 

combinations can be optimized and run in one day with about 5,000 EnergyPlus simulations. 

Running the optimization analysis using Opt-E-Plus is fairly automated and does not take 

an inordinately long time. However, the challenge in performing a building optimization 

Pareto Optimal Front

3
analysis using Opt-E-Plus is to define the baseline building and generate the set of EDMs with

associated costs. Some details may be known at the early design stage, especially where baseline 

data or EDMs from similar past projects can be reused, but rough estimates must be developed in 

areas in which the builder does not have design experience with energy performance or cost 

estimation. The design team needs to agree on these so the EDMs can result in equally weighted 

decisions about the output of the optimization analysis. Fortunately, if baseline assumptions or 

EDM definitions change dramatically, the optimization analysis can be rerun at a minimal cost.

Expected Results

The output of Opt-E-Plus is unique in that it does not just present a single optimal design; 

rather, it shows a range of designs, each of which minimizes energy use for a particular 

economic cost. Typically, life cycle costing is used so that the additional capital costs required 

to implement many EDMs may be recouped through lower utility bills over some analysis 

period. Figure 2 shows the typical output of an Opt-E-Plus analysis. In this figure, each point 

represents a unique combination of EDMs that defines a single potential building design and 

corresponds to an EnergyPlus simulation run. 

 

Figure 2: Typical Opt-E-Plus Analysis Results

Created at NREL

The analysis in Figure 2 selected five designs that are of most interest to the designer. 

The five selected designs progress from the baseline building to the maximum energy savings 

possible (left to right). As the energy savings increase, total life cycle cost first decreases as 

cost-effective EDMs—such as increased insulation and reduced window area—are applied. At 

higher levels of energy savings, less cost-effective EDMs such as photovoltaics (PV) require

4
higher incremental costs to achieve similar incremental energy savings. From left to right these 

designs are:

• The Baseline building (Title 24 defined the baseline in Figure 2). This is included for 

reference in cost and energy savings. 

• The Minimum Life Cycle Cost building. This should be chosen for minimizing life cycle 

cost only. 

• The Neutral Capital Cost building. This should be chosen to maximize energy savings 

with constrained initial capital costs. This point is not determined in Figure 2, rather 

another plot that is Capital Cost versus Energy Savings. 

• The Neutral Life Cycle Cost building. In this design initial investments in energy 

efficiency pay for themselves over a user-defined life cycle analysis period. This is also 

equivalent to the baseline building in life cycle cost. 

• The Maximum Savings point. This represents the maximum energy savings possible 

without regard to cost. 

Expected Impact on the Design Process

Opt-E-Plus generates a set of potential building designs, but none of these points 

represent construction-ready building designs. Rather, the design combinations generated by 

Opt-E-Plus represent high-level combinations of EDMs that capture the interactions between 

different options in energy performance and cost. The primary utility of the Opt-E-Plus analysis 

is to set project level goals and basic strategies to meet those goals based on quantitative, wholebuilding analysis. This includes high-level goals such as payback period and total energy savings 

as well as individual energy and economic goals for each engineering team (fabric, lighting, 

HVAC, etc). These goals are essentially the input assumptions made (e.g., install and 

commission daylighting controls for $0.40/ft2). If a team cannot meet its goals, the project 

coordinator understands that energy savings must be compensated by another building 

component to meet the goal. Currently, the analysis results must be reviewed by experienced 

building engineers and modelers to reduce the impact on the uncertainty in baseline model 

generation and economic impacts. 

Opt-E-Plus 

Retrofit Design Project

The Sacramento Municipal Utility District requested that NREL’s commercial buildings 

group perform an energy optimization analysis for a proposed mixed-use development in 

Sacramento (Macumber 2009). At the beginning of the project, the stated goal was for a zeroenergy building; however, this was more of a “zero-energy vision” than a hard zero-energy goal, 

according to one zero energy buildings definition (Torcellini et al. 2006). In reality, the 

developer was willing to explore a range of energy performance and corresponding economic 

costs, and chose to use net site energy and economic life cycle costs over a 10-year life cycle 

with a 2.3% discount rate to evaluate energy performance. 

The first step was to develop a prototypical baseline building that was verified manually 

for accuracy. Geometry for the baseline model was derived from preliminary architectural

drawings. Separate conditioning, internal gain, and program information was developed for each 

of the office, retail, residential, garage, laundry, storage, and corridor space types. Building 

5
constructions were based on actual constructions from past projects for parking garage, interior 

surfaces, and existing structures. Constructions were based on nominally Title 24-compliant 

constructions for exterior walls, roofs, and slab. Fenestration constructions were based on a 

similar past project. The baseline HVAC system was assumed to be packaged single-zone airsource heat pumps for conditioned spaces and demand-controlled ventilation for the parking 

garage. 

Figure 3: Floor Plan from the Southwest 

The next step was to develop the set of EDMs that might be applied to the baseline model 

to create candidate low-energy models. Examples of EDMs considered in this work include 

increased roof and wall insulation, reduction of lighting and interior equipment loads, and 

installation of solar hot water and PV systems. The cost and performance of certain EDMs, such 

as more insulation or improved windows, were well known from previous projects. However, 

the cost and performance of other EDMs, such as more efficient elevators, were not as well 

known, so the design team developed rough estimates. 

Results 

Zero energy is a difficult goal to meet, especially for buildings with a low roof area to 

floor area ratio; thus, we found no solution to achieve that with the EDMs and costs developed 

during this work. We did find that significant energy savings could be achieved for this project 

under favorable economic criteria. The Minimum Life Cycle Cost building achieved 14.9% 

energy savings at lower capital and life cycle costs than the Baseline building. The Neutral 

Capital Cost building achieved 30.8% energy savings at equal capital costs and lower life cycle 

costs than the Baseline building. The Neutral Life Cycle Cost building achieved 40.0% energy 

savings at higher capital costs but equal life cycle costs to the Baseline building. Finally, the 

Maximum Savings building achieved 63.7% energy savings at higher capital and life cycle costs 

than the Baseline building. A summary of the differential costs and energy savings related to 

each building variation is given in Table 1. 

6
Table 1: Economic Differential and Energy Savings Summary 

Building Variation Incremental 

Capital Cost($)

Incremental Life 

Cycle Cost ($)

Net Energy Intensity

kBtu/ft2 (MJ/m2) 

Percent 

Savings

Baseline NA NA 50.1 (569) NA

Minimum Life Cycle Cost –$432,473 –$661,192 42.6 (484) 14.9%

Neutral Capital Cost $150,722 –$356,961 34.6 (393) 30.8%

Neutral Life Cycle Cost $754,812 $40,250 30.0 (341) 40.0%

Maximum Savings $2,798,237 $1,655,481 20.4 (232) 63.7%

New Construction Office Building

Problem Statement

NREL’s 219,000-ft2 RSF is scheduled to open in the summer of 2010 and will house 822 

employees on NREL’s Golden, Colorado, campus. It will be one of the most energy-efficient 

office buildings in the country, one of the first large net-zero energy buildings (NZEBs), and is 

projected to achieve the U.S. Green Building Council’s (USBGC) Leadership in Energy and 

Environmental Design (LEED) highest rating, LEED Platinum. 

As with most NZEBs, energy was the first priority of the design team. It was determined 

early that for the RSF to reach NZEB status, it first had to be as energy efficient as possible. 

Originally, the contractual requirement was to have annual “pre-PV” energy use intensity (EUI) 

of 25 kBtu/ft2 (284 MJ/m2). The targeted EUI was based on 650 employees and 220,000 ft2. The 

RSF will also house a data center that will service the entire NREL campus. Included in the EUI 

goal is a prorated portion of the data center’s annual energy consumption based on the number of 

employees, which has been increased from 650 to 822. With the increase in employee density 

and space efficiency, the RFP allowed for a EUI goal adjustment on a per person basis. With 

822 employees and 219,000 square feet, the target EUI has been adjusted to 32 kBtu/ft2 with the 

prorated data center, or 35 kBtu/ft2 including the full data center. These EUI requirements were 

included as part of the core requirements for the RSF during the bid process. NREL researchers 

performed Opt-E-Plus analyses and collected data from other high-performing buildings 

nationwide to verify that this energy specification could be met within the project's budget. 

Results

With hard energy goals established at the beginning of the project, the design team 

worked to integrate building form, lighting, HVAC, computers, server equipment, and other 

subsystems. One design aspect of particular interest is the building’s lazy-H configuration on a 

long east-west axis. Because of the layout of the building, the occupied zones are 60 feet wide. 

This narrow floor plate offers daylighting and natural ventilation to a large area. 

If the two wings were set side by side, the building would have an aspect ratio of 13.5. 

As the aspect ratio increases from 1 (a square footprint building), to 13.5 and beyond, the 

building envelope area increases significantly. This higher aspect ratio allows daylighting and 

natural ventilation in a larger portion of the occupied building. The drawback is in increased 

heat transfer through the envelope. NREL researchers used Opt-E-Plus to vary the aspect ratio 

of a prototypical building to assess the design team’s narrow floor plate design decision. 

7
Researchers also considered reduced plug load and lighting intensities, daylighting 

controls, decreased window-to-wall ratios, window shading, increased exterior wall and roof 

insulation levels, more efficient window constructions, natural ventilation, and PV. Opt-E-Plus 

enabled the benefits of each strategy to be weighed against its costs and included interactions 

between technologies. Figure 3 shows the swoosh graph produced by Opt-E-Plus. The Y-axis is 

no longer percent savings, but is absolute Net Site Energy, as the goal was to reach 0.0 kBtu/ft2

(0.0 MJ/m2

) (with PV). The buildings in Figure 4 become more efficient as they move from 

right to left. The upper left corner collection of simulations represents the results that include PV 

fully covering the roof (and the panel efficiency was adjusted to achieve net-zero energy). These 

points achieve the net-zero energy goals, but also incur a higher life cycle cost than the baseline. 

Figure 4: RSF Optimization Results 

Net Site Energy Intensity [kBtu/ft^2]

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0

Total Life Cycle Cost (TLCC) [$]

12,000,000

11,000,000

10,000,000

9,000,000

8,000,000

7,000,000

6,000,000

5,000,000

4,000,000

The conclusion of this study was that the higher aspect ratios did generally reduce energy 

use relative to their lower aspect ratio counterparts; however, the increased envelope area also 

increased first capital costs. The aspect ratio of 13.5 chosen for the RSF was beyond the point 

where energy savings paid back for the additional envelope costs. For a building with true netzero energy goals, however, this was still appropriate relative to other more expensive measures 

such as PV. This analysis does not include nonenergy benefits to a narrow floor plan such as 

increased views to the outdoors. 

The Minimum Life Cycle Cost building achieved 46.4% energy savings at lower capital 

and life cycle costs than the Baseline building. The Neutral Capital Cost and Neutral Life Cycle 

Cost building achieved 51.4% energy savings at lower capital and life cycle costs than the 

Baseline building. (In both Capital Cost and Life Cycle Cost, there was not a true Neutral Cost 

building; the neutral cost point fell where the simulations begin to include PV.) Finally, the 

Maximum Savings building achieved 103.2% energy savings at higher capital and life cycle 

8
costs than the Baseline building. A summary of the differential costs and energy savings related 

to each building variation is given in Table 2. 

Table 2: Economic Differential and Energy Savings Summary

Building Variation Incremental 

Capital Cost($)

Incremental Life 

Cycle Cost ($)

Net Energy 

Intensity (kBtu/ft2)

Percent 

Savings

Baseline NA NA 42.9 NA

Minimum Life Cycle Cost –$1,515,508 –$3,343,683 23.0 46.4%

Neutral Capital Cost –$1,183,514 –$3,105,941 19.7 51.4%

Neutral Life Cycle Cost –$1,183,514 –$3,105,941 19.7 51.4%

Maximum Savings $5,321,453 $2,502,860 –1.4 103.2%

Nationwide Design Guides

The American Society of Heating, Refrigerating and Air-Conditioning Engineers 

(ASHRAE) is developing tools to help achieve its vision of market-viable NZEBs by 2030 

(Jarnagin et al. 2008). To this end, ASHRAE, in conjunction with the American Institute of 

Architects, the U.S. Department of Energy, the USGBC, and Illuminating Engineering Society, 

has been developing Advanced Energy Design Guides (AEDGs) to chart technically feasible, 

cost-effective design paths leading to net-zero energy status for commercial buildings. 

Thus far, six AEDGs have been completed, providing prescriptive design 

recommendations to reach 30% energy savings compared to buildings that meet the minimum 

requirements of the ASHRAE 90.1-1999 code. Recommendations are provided for eight 

ASHRAE climate zones across the United States. Each AEDG is accompanied by a Technical 

Support Document (TSD) that describes the process and methodology used to develop the 

AEDGs recommendations, including detailed descriptions of modeling assumptions and results. 

A set of 50% energy saving AEDGs is currently under development, and several TSDs are 

already finished. Two of these, analyzing energy use in medium-box (40,000–50,000 ft2

) 

general merchandise (Hale et al. 2009) and grocery stores (Leach et al. 2009), employed 

optimization, through Opt-E-Plus, as the primary tool to select 50% energy savings design 

packages. 

The steps to complete the nationwide design guides were similar to the other cases. First, 

we defined a prototypical building model containing the space types, programs, and end uses 

required for each building type. Hourly TMY2 files from large cities in the climate zones under 

consideration were used to represent spatio-temporal variability in meteorological conditions. 

The representative cities include 1A-Miami, 2A-Houston, 2B-Phoenix, 3A-Atlanta, 3B-Las 

Vegas, 3C-San Francisco, 4A-Baltimore, 4B-Albuquerque, 4C-Seattle, 5A-Chicago, 5B-Denver, 

6A-Minneapolis, 6B-Helena, 7-Duluth, 8-Fairbanks. The prototype buildings were then tailored 

to meet the climate-specific requirements of ASHRAE 90.1. Prescriptive requirements were 

taken from ASHRAE 90.1-2004 (ASHRAE 2004) and ASHRAE 62.1-1999 (ASHRAE 1999). 

The design team assembled a list of EDMs intended to reduce building energy 

consumption, specifying performance and costs based on the available literature (Hale et al. 

2009; Leach et al. 2009). This list was peer reviewed by industry partners to verify the 

assumptions for each EDM. The EDMs were common in the two buildings except for 

refrigeration in the grocery store and high plug loads for the general merchandise store. Finally, 

the use of Opt-E-Plus determined EDM combinations that best balanced net site energy savings 

and Five-Year Total Life Cycle Cost. 

9
Results

The optimizations typically resulted in a swoosh graph that had more than 50% energy 

savings. In cases where PV was not required to reach 50% energy savings, the first point on the 

Pareto front to meet or exceed the energy savings target was selected as the low energy design. 

When PV was included in the design, energy savings were quite significant, because the EDM 

specified covering 60% of the net roof area (roof area minus skylight area). To select a 50% 

energy savings design, the PV was scaled back using a postprocessing algorithm until the energy 

target was exactly met. Figure 5 shows an example where this postprocessing was applied, the 

high plug load general merchandise store in climate region 3B (Las Vegas, Nevada). The 

selected 50% energy saving building is marked by the “+” surrounded by an orange circle. 

Figure 5: Example Opt-E-Plus Output With PV Post-Processing: Climate Zone 3B 

(Las Vegas, Nevada), High Plug Load Scenario

The results suggest that 50% net site energy savings compared to a minimally compliant 

ASHRAE 90.1-2004 case could be achieved in general merchandise (low and high plug load 

cases) and grocery stores in almost all cases without relying on renewable energy generation 

such as PV panels. The main exceptions occurred in the high plug load general merchandise 

stores in climate zones 2B and 3B where plug/process loads represent a large fraction of the 

energy demand in the baseline building, since we only investigated one minor measure to 

manage those loads. The following list contains some EDMs the optimization can deliver as 

recommendations for all climate zones: 

• Reduce lighting power density by 47%, and install occupancy sensors in the active 

storage, mechanical room, restroom, and office zones. 

• Equip rooftop HVAC units with high-efficiency fans. 

• Install daylighting sensors tuned to a 46.5 fc (500 lux) set point. 

• Reduce south façade window-to-wall ratio by 50%. 

• Replace baseline exterior walls with better insulated constructions. 

10
• In grocery stores, replace baseline frozen food and ice cream refrigerated cases with 

efficient, vertical models with doors and hot gas defrost. 

• In grocery stores, replace baseline meat display cases with models that have efficient 

fans, anti-sweat heater controls, electric defrost, and sliding doors. 

EDMs for which selection varied by climate zone included:

• Skylights were selected in warm and hot climates where there is ample sunlight for 

daylighting. 

• High coefficient of performance (a 20% increase over baseline) HVAC rooftop units 

were selected in all but the cold and marine climates, which have low cooling loads. 

• Infiltration reduction measures (front entrance vestibule and envelope air barrier) were 

selected often, especially in humid and cold climates.

The suggestions are very “high-level” criteria needed for each major subsystem. Design 

teams can take these suggestions and decide which specific technologies can cost-effectively

provide the required energy savings performance for their specific projects. 

Conclusions

The optimization platform was used in three scenarios to provide feedback to design 

teams on varying design pathways. In the case of the retrofit analysis, the original building 

energy goals were shown to be unachievable, but provided the design team with valuable 

information to set new goals. In the new construction, the aspect ratio was verified and shown to 

be a viable solution. Finally, in the nationwide design guides, the energy savings and economic 

goals were shown to be achievable.

Opt-E-Plus has helped researchers study the relative impacts of various design decisions 

at a high level early in the design process. The results generated give design pathway guidance 

through high-level advice for a developer, architect, and design team. As seen in the examples 

above, the results can be used to persuade investors to agree to higher capital costs due to life 

cycle savings from reduced utility bills. The architect can also balance desires for more windows 

with the project’s goals to reduce first costs and energy use. A design team leader can determine 

which subsystems need to perform better in order to meet the overall energy goals. The 

integration of economics in the energy simulation results provides justification for the EDMs and 

helps produce an equally weighted evaluation. The analysis is sensitive to errors in assumptions 

about performance and cost of the various EDMs. Future versions of the optimization platform 

plan to incorporate uncertainties of performance and cost throughout the whole design process 

and optimizations, along with integration of other simulation analysis tools to augment the 

analysis.

11
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Definition. Golden, Colo.: National Renewable Energy Laboratory. 

Turner, C., and M. Frankel. 2008. Energy Performance of LEED for New Construction 

Buildings White Salmon, Wash.: New Buildings Institute. 

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1. REPORT DATE (DD-MM-YYYY)

August 2010

2. REPORT TYPE

Conference paper

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4. TITLE AND SUBTITLE

Commercial Building Design Pathways Using Optimization Analysis: 

Preprint

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

N. Long, A. Hirsch, C. Lobato, and D. Macumber

5d. PROJECT NUMBER

NREL/CP-550-48264

5e. TASK NUMBER

BEC7.1390

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

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

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REPORT NUMBER

NREL/CP-550-48264

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NREL

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National Technical Information Service

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13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

Whole-building simulation and analysis has demonstrated a significant energy savings potential in a wide variety of 

design projects. Commercial building design, however, traditionally integrates simulation and modeling analyses too 

late in the design process to make a substantial impact on energy use. The National Renewable Energy Laboratory 

(NREL) commercial building group created an optimization platform called Opt-E-Plus that uses multivariate and 

multi-objective optimization theory to navigate a large parameter space and find economically valid, energy-saving 

solutions. The analysis results provide designers and engineers valuable information that influences the design. The 

pathways are not full “construction ready” design alternatives; rather, they offer guidance about performance and cost 

criteria to reach a range of energy and economic goals. Having this knowledge early in the design phase helps 

designers establish project goals and direct the design pathway before they make important decisions. Opt-E-Plus 

has been deployed on several projects, including a retrofit mixed-use building, a new NREL office building, and 

several nationwide design guides. Each of these projects had different design criteria, goals, and audiences. In 

each case the analysis results provided pathways that helped inform the design process. 

15. SUBJECT TERMS

commercial building design; optimization; opt-e-plus; optimization analysis

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