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NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Automated Multivariate 

Optimization Tool for 

Energy Analysis 

Preprint 

P.G. Ellis, B. Griffith, N. Long, and P. Torcellini 

National Renewable Energy Laboratory 

D. Crawley 

U.S. Department of Energy

To be presented at IBPSA SimBuild 2006 Conference 

Cambridge, Massachusetts 

August 2−4, 2006 

Conference Paper 

NREL/CP-550-40353 

July 2006 
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AUTOMATED MULTIVARIATE OPTIMIZATION TOOL 

FOR ENERGY ANALYSISa

Peter G. Ellis1, Brent T. Griffith1, Nicholas Long1, Paul Torcellini1, and Drury Crawley2

1

National Renewable Energy Laboratory, Golden, CO 

2

United States Department of Energy, Washington, DC 

 

a This work has been authored by an employee or employees of the Midwest Research Institute under Contract No. DE-AC36-

99GO10337 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the 

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ABSTRACT

Building energy simulations are often used for trial-anderror evaluation of “what-if” options in building 

design—a limited search for an optimal solution, or 

“optimization.” Computerized searching has the 

potential to automate the input and output, evaluate 

many options, and perform enough simulations to 

account for the complex interactions among 

combinations of options. This paper describes ongoing 

efforts to develop such a tool. The optimization tool 

employs multiple modules, including a graphical user 

interface, a database, a preprocessor, the EnergyPlus 

simulation engine, an optimization engine, and a 

simulation run manager. Each module is described and 

the overall application architecture is summarized. 

INTRODUCTION

Building energy simulations are often used for trial-anderror evaluation of “what-if” options in building 

design—a limited search for an optimal solution. With 

today’s computer power, the bottleneck is no longer 

simulation run time, but rather the human time to handle 

input and output. Human-driven methods are 

inefficient, and require skills, experience, and time. 

Computerized optimization has the potential to 

automate the input and output, evaluate many options, 

and perform enough simulations to account for the 

complex interactions among combinations of options. 

This paper describes ongoing efforts to develop such an 

automated optimization tool. 

Building design problems are inherently multivariate 

and multicriteria. Multivariate optimization is much 

more difficult than the simpler problem of minimizing a 

single variable. The objective, or performance index, 

also must include energy performance and the cost 

implications of design options. However, as pointed 

out by Papamicheal (1993), the relative importance of 

cost and performance cannot be explicitly specified 

because it represents a qualitative judgment. The 

implication is that the optimization search is best 

formulated as a multicriteria, or multiobjective, search 

for a set, or Pareto-optimal front, of optimal solutions. 

Figure 1 uses one possible choice of metrics for cost 

and performance (solutions that are down and to the left 

are better) to diagram a Pareto front. A designer who is 

presented with such results then has a range of possible 

solutions (which are all optimal) that can be used to 

inform decision-making. The preferred search 

algorithms for finding the Pareto-optimal front can 

separately and simultaneously minimize both cost and 

performance. This is opposed to the more common 

approach of attempting to aggregate and weight 

different metrics into a single performance index. 

Numerous researchers have studied the application of 

optimal searches for building design. The GenOpt 

program implements a large number of search 

algorithms. The most prominent is called “Generalized 

Pattern Search Hooke-Jeeves” (Wetter 2004). 

Gradient-based methods are not well suited for building 

design applications. Wetter and Wright (2003) point 

out that the approximate solutions produced by energy 

simulation can lead to discontinuous results, which 

cause problems for gradient methods. For buildings, 

search methods need to handle discrete variables and 

should attempt to identify a broad portion of the Paretooptimal front. Genetic algorithms are applicable to 

discrete variables and have been studied (in the 

buildings context) by Wright et al. (2002), Huang and 

Lam (1997), Coley and Schukat (2002), and Caldas and 

Norford (2003). Wetter and Wright (2003) proposed 

1
combining single objective genetic algorithms with 

generalized pattern search algorithms. 

Figure 1 Pareto front of optimal solutions

OPTIMIZATION TOOL

The authors developed an optimization tool as a 

research platform to answer broad, energy-related 

questions about the commercial building sector for the 

U.S. Department of Energy (DOE). In particular, DOE 

has a goal to set the conditions for marketable zeroenergy buildings by 2025. Whole-building energy 

modeling and optimization are considered important 

tools for achieving this goal, as they can identify 

common solutions for energy efficiency that can be 

replicated across the commercial building sector. 

This project grew out of a related study, also 

commissioned by DOE, which is referred to as the 

Assessment of Commercial Sector Opportunities (see 

Griffith and Crawley 2006 in these proceedings). To 

perform the Assessment, we developed software tools 

for automatically creating and running many 

simulations. The Assessment automatically generated 

thousands of EnergyPlus input files that swept a variety 

of design options over every TMY2 weather file 

location in the country. More than 200,000 simulations 

for the Assessment were run on NREL’s computing 

resources. Much of its application architecture was 

reused and further developed to form the underpinnings 

of the optimization tool. 

Because extensive computing resources are required for 

useful optimizations, the optimization tool has been 

mainly developed as an in-house research tool that uses 

a supercomputer. 

Overview 

The optimization tool employs multiple modules, 

including: (1) a graphical user interface (GUI) for 

selecting options and viewing results, (2) a database for 

storing component performance data and costs, (3) a 

preprocessor to convert high-level input parameters into 

a detailed building model, (4) the EnergyPlus wholebuilding simulation engine to analyze the model, (5) an 

optimization engine to select design options, and (6) a 

simulation run manager to handle simulation runs on 

different computing resources. The Assessment

pioneered the initial efforts for developing and 

integrating the preprocessor, input database, simulation 

engine, and simulation run manager. Each module is 

described in detail below and diagrammed in Figure 2. 

Figure 2 Block diagram of optimization tool

The program architecture is modular to allow multiple 

programmers to work at the same time. Modules are 

programmed in Fortran95 or in object-oriented Delphi. 

Graphical User Interface 

The GUI features a building creation wizard, an options 

browser, and a results browser. The building creation 

wizard presents a series of tabs with high-level 

parameters that define the overall type and location of 

the building. The options browser allows the user to 

select from 40 major design options that are grouped 

into categories of program, form, fabric, and equipment 

(see Figure 3). Program parameters include lighting 

density, plug and process load density (electrical 

equipment), people density, infiltration rate, illuminance 

setpoints for automatic dimming controls, and density 

of tubular daylighting devices (TDDs). Form 

2
parameters include aspect ratio, orientation, window 

glazing fraction, and overhang depth. Fabric 

parameters include choice of constructions for exterior 

walls, interior walls, roofs, windows, and skylights. 

Equipment parameters include choice of HVAC system 

type and photovoltaic system efficiency. All options are 

selected as discrete values. Cost data are also 

associated with each option. 

The GUI provides a results browser that renders all the 

simulations on a chart of percent energy savings versus 

percent cost savings (see Figure 4). Each point 

represents an individual annual simulation. The user 

simply clicks on a point in the graph to view the 

individual results for that simulation. The 3D 

wireframe view (DXF) of the building model is also 

available. 

The GUI is coded in Delphi 7 and uses model-driven 

architecture to take advantage of many vendor-provided 

software components for database interactions, GUI 

controls, DXF views, and fast array and matrix math. 

Simulation Engine 

The optimization platform uses EnergyPlus as the 

simulation engine. EnergyPlus is a whole-building 

energy simulation program developed by DOE 

(Crawley et al. 2004) that can model the complex 

interactions that are important for optimization. 

EnergyPlus can also simulate many state-of-the-art and 

cutting-edge technologies that will be used in 

tomorrow’s zero-energy buildings. The ability to model 

technologies such as TDDs, radiant heating, and 

underfloor air distribution, along with features for 

thermal comfort, will allow the optimization tool to 

include these options in the simulations. (Some of these 

technologies are not yet implemented in the GUI and 

preprocessor.) 

Because the optimization process is computationally 

intensive and demands hundreds (or even thousands) of 

individual simulation runs, we have looked for ways to 

decrease the run time of EnergyPlus simulations to 

expedite the overall process. Adjusting factors such as 

minimum system time step, minimum temperature 

convergence, and maximum number of warm-up days 

significantly decreases the run time of the program by 

up to a factor of 10 with little penalty in accuracy. We 

also use multipliers for thermal zones to help reduce run 

times. This greatly reduces the run times for multistory 

buildings and buildings with hundreds of TDDs. We 

continue to explore further opportunities to decrease the 

simulation run times. 

Preprocessor 

Because simulation engines such as EnergyPlus require 

detailed input data about the building, we use a 

preprocessor to reduce the low-level input data to a set 

of high-level input parameters. Unlike most automated, 

macro-based procedures that search and replace 

parameters one by one, the high-level parameters can 

have a one-to-many relationship. The preprocessor 

implements a large number of deterministic assignments 

that map high-level parameters to the low-level 

simulation input. For instance, a parameter to vary the 

number of floors can completely change the number, 

size, and location of almost every surface in the 

EnergyPlus model. It also changes the number of 

thermal zones and configuration of the HVAC system. 

Finally, it can affect the HVAC sizing. 

High-level parameters are implemented in the 

preprocessor for every option in the GUI options 

browser. We use an XML text file to exchange data 

between the GUI and the preprocessor based on an 

XML Schema that we developed for this purpose. The 

preprocessor then uses the high-level parameters to 

“autobuild” the EnergyPlus input file, including all the 

envelope geometry, zoning, internal loads, schedules, 

and HVAC system. Deterministic assignments are used 

to default simulation input details that are not explicitly 

defined by the parameters. 

Database 

The database module serves multiple purposes by 

storing design options and input parameters selected in 

the GUI, input libraries for EnergyPlus, cost data, 

simulation run queue, and simulation results. All data 

except for the EnergyPlus input libraries (which are text 

files) are stored in a relational database management 

system. 

The EnergyPlus input libraries comprise a collection of 

macro files, or include files, that provide segments of 

EnergyPlus input for location-dependent input objects, 

schedules, HVAC systems and component performance 

data, and report variables. Some macro files that were 

distributed with the release version of EnergyPlus are 

also included. 

We consulted practicing architects and engineers to 

help formulate cost estimates. We also used data from 

RSMeans (2004) with regional adjustment factors. 

The database module has an input mechanism to add or 

change data and allows for user input of cost 

information. 

3
After simulations are completed, the results are also 

stored in the database so that individual simulation 

results can be browsed in the GUI. 

Optimization Engine 

The optimization engine module is the heart of the 

optimization tool. It determines which design options 

are to be simulated and then analyzes the results in the 

context of the performance objective that is to be 

minimized. The optimization engine then decides 

which options to keep and which to change before it 

launches a new set of simulations. 

Early pilot work for the project used GenOpt (Wetter 

2004) as the optimization engine. GenOpt is a generalpurpose engine that enables a selection of optimization 

algorithms, including generalized pattern search, 

gradient search, particle swarm, and multidimensional 

parametric runs (enumeration) to be run. We later 

developed our own optimization engine that supports 

multiple optimization algorithms, including full 

enumeration and sequential search. Because the 

algorithms are programmed in a modular fashion within 

the optimization engine, more algorithms can be added 

easily. 

The full enumeration, or “brute force,” algorithm 

evaluates every possible combination of options. It is 

guaranteed to always identify the true optimal solution, 

but it is the most time-consuming and computationally 

intensive algorithm. Full enumeration is primarily used 

to check the results of the other algorithms. 

The sequential search algorithm is the same 

optimization method used in BEOpt (Christensen et al. 

2006). The method was probably first described by 

Meier (1982) and used in a different, whole-sector 

context. It is similar to one used by Davis Energy 

Group in a Pacific Gas and Electric ACT2 project 

(DEG 1993) and to the “energy code multiplier 

method” available in EnergyGauge-Pro (FSEC 2001). 

Christensen developed this method specifically to 

search for a “path” to zero-energy homes. The Paretooptimal front is defined by connecting the points for 

building designs that achieve various levels of energy 

savings at minimal cost (establishing the lower bound of 

results from all possible building designs). The search 

technique moves along the path in steps. At each step 

along the path, individual simulations are performed to 

evaluate all options across a range of categories (wall 

type, ceiling type, window glass type, HVAC type, etc.) 

and searches for the most cost-effective option. Based 

on the results from the previous step, the most costeffective option is selected as an optimal point on the 

path and put into a new building description. The 

process is then repeated. 

The search technique handles special cases that are 

caused by interactions between options (beyond just the 

diminishing returns accounted for in the basic 

sequential search technique). Special cases with 

negative interactions are handled by looking back along 

the path and continually re-evaluating previously 

rejected options to properly identify the potential of 

large-savings options and options that involve trade-offs 

between categories. The search technique does not 

assume that once an option is selected that it stays 

selected. The technique also tracks points from 

previous steps and checks to see whether they may be 

better results than the current step. Positive interactions 

are accommodated by allowing the user to define 

combined options (which are linked to ensure that such 

potentially synergistic combinations are evaluated 

during optimization). The method has been validated 

by comparison to full enumeration. 

The sequential search technique has several advantages. 

It searches for points near the Pareto-optimal front in a 

single optimization analysis, i.e., minimum-cost 

building designs at different energy savings levels, not 

just a global optimum. It also stores multiple nearoptimal designs that are identified at each particular 

energy savings level. This may provide interesting 

design alternatives. The method is amenable to using 

distributed computing because for each step, an entire 

set of simulations can be run at the same time. 

Disadvantages of the technique have not been explicitly 

identified. Areas to investigate may include coarse and 

fixed resolution for continuous parameters, scalability 

problems for very large numbers of design options, and 

its inability to ensure that synergistic but separate 

options are selected. These are all critical issues for 

shifting from residential to commercial analysis. The 

advantage of the technique is that relatively few 

iterations are required to arrive at the solution. 

The resulting tool has the advantages of providing nearoptimal solutions. 

The sequential search allows for multi-objective and 

multicriteria optimizations. The principle behind the 

algorithms is to find the steepest slope from the 

previous point. Currently the slope is the change in cost 

versus the change in energy. However, if needed, more 

objectives can be added and tensors could be used to 

calculate the slopes. 

Simulation Run Manager 

Depending on the number of options that are selected in 

the GUI, the processor time needed for the optimization 

engine to resolve an answer can be very long. 

However, because each step in the optimization process 

4
uses multiple independent simulations, it favors a 

distributed computing approach. 

NREL has two computing resources for distributed 

computing. The Computational Sciences Center 

maintains a Linux cluster, dubbed Lester, of 126 dualprocessor nodes that run the 64-bit Linux operating 

system. We can use a custom 64-bit build of 

EnergyPlus for Linux to run up to 252 simultaneous 

simulations on the cluster. In reality, the cluster is 

shared with other users at NREL, so actual availability 

is less. 

NREL also maintains a distributed computing network 

that uses the Condor workload management system 

(Thain et al. 2005). The Condor system identifies idle 

computers on the laboratory’s local area network and 

takes advantage of the unused processing power to run 

jobs in the background. We currently have about 40 

Windows machines enrolled in the Condor network, but 

we plan to increase this number in the future. 

A simulation run manager handles the distribution of 

jobs launched by the framework and farms them out for 

processing on the computing resources, including 

Lester, Condor, and the user’s local machine. The 

simulation run manager also monitors the status of 

running jobs and retrieves the simulation results when 

the job is done. 

Lester and Condor are valuable computing resources 

that have allowed us to develop and test the 

optimization engine with a reasonable turnaround time 

for results. For example, an optimization that requires 

18 iterations for 545 simulations (each simulation can 

take up to 2 minutes) can be completed in 2.5 hours, 

which averages to approximately 16.5 seconds per 

simulation. 

Although distributed computing certainly expedites the 

optimization process, it is not required to run the tool. 

The optimization will just take longer to run the 

simulations individually. 

VALIDATION

The main validation task is to verify that the input data 

are accurate and that the optimization tool reliably 

identifies the true optimum combination of parameters 

and design options. We have used the full enumeration 

algorithm, which will always find the true optimum 

(among the selected options), to check the results of the 

other optimization algorithms. The outcome of this 

showed that the sequential search reasonably 

approximates the Pareto-optimal front; enumeration did 

not yield any surprises. However, there are practical 

limitations to using full enumeration because the 

number of cases to evaluate becomes far too large for 

most real-world problems. 

Thus far our testing has identified some deficiencies in 

the sequential search algorithm under certain scenarios. 

Competing design options can sometimes result in the 

true optimum solution not being correctly identified. 

One technology may not be useful unless it is combined 

with two or more other technologies. For example, 

TDDs by themselves may not be more efficient unless 

they are combined with daylighting controls. 

Fortunately, in this particular case, the sequential search 

algorithm will find this combination because daylighting 

is always an advantage. However, other combinations 

may not be found. The issue is in the number of 

degrees of freedom, which is currently limited to one. 

The optimization tool is also being tested in a realworld application under another NREL project by using 

the tool in the design phases of a small retail building. 

Cost data are still problematic, especially for HVAC 

systems and equipment. Costs are also volatile. As 

mentioned earlier, cost is key to the entire optimization 

procedure. Working on real-world projects is an 

important approach for collecting cost data. 

CONCLUSION

An optimization tool has been implemented to evaluate 

the cost and performance trade-offs to support decisionmaking on commercial building design projects. The 

tool currently requires considerable computing 

resources and is intended for in-house research to assist 

in DOE-funded research in support of the goal of zeroenergy buildings. As computing power increases, 

developing a non-research version of the framework for 

practitioners in the private sector will become more 

feasible. Collecting and verifying cost and performance 

input data remain important challenges. The sequential 

substitution search algorithm is effective; however, 

improved search algorithms and more efficient methods 

of validating them are needed. 

ACKNOWLEDGMENTS

This work was supported by the Building Technologies 

Program within the Office of Energy Efficiency and 

Renewable Energy at the U.S. Department of Energy. 

The authors would like to thank the numerous people 

who contributed effort, ideas, and reviews to the study, 

including Ron Judkoff, Craig Christensen, Mary Colvin, 

and Michael Deru. 

REFERENCES

Caldas, L.G. and Norford, L. 2003. “Genetic algorithms 

for optimization of building envelopes and the 

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design and control of HVAC systems.” Journal of 

Solar Energy Engineering. August 2003, Vol. 125, 

pp. 343−351. 

Christensen, C. et al. 2006. “BEOpt: software for 

identifying optimal building designs on the path to 

zero net energy.” Proceedings of ISES 2005 Solar 

World Congress, Orlando, FL, August 6−12, 2005. 

Coley, D.A. and Schukat, S, 2002. “Low-energy design: 

combining computer-based optimization and 

human judgment.” Building and Environment 37: 

1241−1247. 

Crawley, D.B. et al. 2004. “EnergyPlus: an update.” 

Proceedings of the SimBuild 2004 Conference, 

August 4−6, 2004, Boulder, CO. 

DEG. 1993. ACT2 Stanford Ranch Site Final Design 

Report. Davis, CA: Davis Energy Group. 

FSEC. 2001. EnergyGauge Pro. Cocoa, FL: Florida 

Solar Energy Center. http://energygauge.com/ 

FlaRes/features/pro.htm. 

Griffith, B., and Crawley, D. 2006. “Methodology for 

analyzing the technical potential for energy 

performance in the us commercial buildings sector 

using detailed energy modeling.” Proceedings of 

the SimBuild 2006 Conference, August 2006. 

Huang, W., and Lam, H.N. 1997. “Using genetic 

algorithms to optimize controller parameters for 

HVAC systems.” Energy and Buildings 26 (1997), 

pp. 277−282. 

Meier, A.K. 1982. Supply Curves of Conserved 

Energy. Lawrence Berkeley National Laboratory, 

LBL-14686. 

Papamichael, K. 1993. “The limits of intelligence in 

design.” For the 4th International Symposium on 

System Research, Infomatics and Cybernetics, 

Baden-Baden, Germany, August 3−4. Also LBL31742. 

RSMeans. 2004. 2005 Cost Data. RSMeans. Kingston, 

MA. Along with other titles including Assemblies 

Cost Data, Square Foot Costs, Facilities 

Maintenance and Repair Cost Data, Green 

Building: Project Planning & Cost Estimating. 

Thain D., Tannenbaum, T., and Livny, M. 2005. 

“Distributed Computing in Practice: The Condor 

Experience.” Concurrency and Computation: 

Practice and Experience, Vol. 17, No. 2−4, pp. 

323−356. 

Wetter, M. 2004. GenOpt Generic Optimization 

Program User Manual, Version 2.0.0. Berkeley, 

CA: Lawrence Berkeley National Laboratory. 

Wetter, M. and Wright, J. 2003. “Comparison of a 

generalized pattern search and a genetic algoritm 

optimization method.” Eighth International IBPSA 

Conference. August 2003, Eindhoven, The 

Netherlands. 

Wright, J.A., Loosemore, H.A., and Farmani, R. 2002. 

“Optimization of building thermal design and 

control by multi-criterion genetic algorithm.” 

Energy and Buildings 43 (2002) 959−972. 

6
Figure 3 Screen shot of the options browser 

Figure 4 Screen shot of the results browser

7
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14. ABSTRACT (Maximum 200 Words)

Building energy simulations are often used for trial-and-error evaluation of “what-if” options in building design—a 

limited search for an optimal solution, or “optimization.” Computerized searching has the potential to automate the 

input and output, evaluate many options, and perform enough simulations to account for the complex interactions 

among combinations of options. This paper describes ongoing efforts to develop such a tool. The optimization tool 

employs multiple modules, including a graphical user interface, a database, a preprocessor, the EnergyPlus 

simulation engine, an optimization engine, and a simulation run manager. Each module is described and the overall 

application architecture is summarized. 

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optimization; building energy simulation; energyplus 

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