coupling trnsys and matlab for genetic algorithm ... · coupling trnsys and matlab for genetic...

COUPLING TRNSYS AND MATLAB FOR GENETIC ALGORITHM OPTIMIZATION INSUSTAINABLE BUILDING DESIGN

Marcus JonesVienna University of Technology, Vienna, Austria

ABSTRACTIncorporating energy efficient features into sustainable

buildings is cost effective during the design phase. In de-signing a sustainable building, the designer is faced witha staggering amount of parameters, conditions, and ob-jectives. However, costs and limited time do not allowthe designer to effectively evaluate all candidate build-ing designs. Genetic algorithms are interesting for thisproblem given their ability to optimize complex multi-objective non-smooth optimization problems. The appli-cation of genetic algorithms to the problem of sustainablebuilding design has been developed and will be presented.A method of interfacing TRNSYS and the Matlab geneticalgorithm toolbox has been tested by application to twosimple energy design problems. The approach of cou-pling the detailed modeling capabilities of TRNSYS andgenetic algorithm routines in Matlab is powerful combina-tion in the search for optimal sustainable building designs.

MOTIVATIONSustainable building certification programs are becom-

ing ever more popular, with their principles moving fromvoluntary rating levels into legislated requirements. Themotivation is clear, sustainable building construction, op-eration, and decommissioning will play an important rolein a sustainable future. However, sustainable building de-sign is a highly complex process.

1110 1011 0011

0110 1001 1011

0001 1101 0101

Genotype

Phenotype

Figure 1: Building design and evolution

The conditions in which the building is designed andoperated should be well understood. These are bound-

aries which the designer has little or no control over, andinclude the weather, surroundings, availability of energyresources, economic considerations, and building loads.The second aspect is the design of the building and sys-tem defined by the parameters which the designer doeshave control over. These include the parameters of thebuilding envelope shape and materials, the energy systemcomponents and how the system is controlled and oper-ated. Finally, the design objectives should be clear. Spe-cific objectives include comfort levels, operating and in-vestment costs, carbon reduction, and aesthetics.

In mathematical terms, this is a multi-objective opti-mization problem, and can be formalized as;

min~x

[µ1(~x),µ1(~x), . . . ,µn(~x)]

where ~x is a vector in design space subject to the designconstraints (a candidate solution), and µi is the i-th ob-jective function, a component of the ~µ solution vector.The solution to the problem is the set of Pareto optimum(Bui and Alam 2008). In optimization problems, the de-sign variables define a solution space which is methodi-cally searched in order to find the best candidate solutions.Building design problems are complex, often non-smoothin the solution space, and multi-objective. Because of theinteracting nature of the design variables, unintuitive so-lutions exist which a designer may not consider. Becauseof the number of solutions, the solution space must be ef-ficiently searched. Approaches to finding good solutionsinclude past experience, trial and error, simple calcula-tions, and building simulation. Because of the number ofdesign variables and their interacting and dynamic nature,building simulation is becoming a more popular tool inthe construction industry, becoming integrated within thearchitectural process.

For this type of optimization problem, genetic algo-rithms provide a robust and efficient optimization method(Wetter 2004). Genetic algorithms are parallelizable, somany function evaluations can be distributed across mul-tiple processors (Eiben and Smith 2007). Because ge-netic algorithms have an element of random search, un-intuitive but very effective solutions can be found (Keaneand Brown 1996).

GENETIC ALGORITHMSIn nature, evolution serves as the mechanism in search-

ing for the best candidate solutions to the complex prob-

Third German-Austrian IBPSA Conference

Vienna University of Technology

Building Performance Simulation in a Changing Environment - A. Mahdavi / B. Martens (eds.) - 9

lem of survival. In abstract terms, an organism can be de-fined by DNA. Individuals which are best adapted to theenvironment propagate their DNA to further generations.Small random variations create new traits, to increase di-versity in the population. This mechanism has producedthe incredible variety of organisms in nature, excellentsolutions to the highly complicated problem of survival.This same mechanism can be applied to sustainable build-ing design.

Figure 2 shows a generic genetic algorithm. First, aninitial population is defined, usually containing randomlygenerated individuals. Each individual in the populationis then evaluated. In the case of building simulation, thisis the execution of the simulation. The number of evalua-tions and the average time required for one evaluation typ-ically defines the time required for the overall optimiza-tion run. The results of the evaluations are then assigneda fitness level, which represents the individual’s proxim-ity to the optimum. Individuals which best meet the ob-jectives of the problem are then selected to propagate thenext generation, to form the parent population. The par-ents then recombine to form the unmutated population.Random variation is applied to this population to producethe next generation. This cycle repeats until a stoppingcondition is reached a fixed number of generations or anumber of generations in which successive iterations donot produce significant improvement.

Successful design of a genetic algorithm involves min-imizing the time required to find the optimum solution. Asuccessful algorithm could depart significantly from thegeneric algorithm described above depending on the ap-plication, with many parameters and strategies to be con-sidered.

INTERFACE TO SIMULATIONThe simulation tool under consideration is TRNSYS,

v.17 (Solar Energy Laboratory, Univ. of Wisconsin-Madison 2009). The optimization will be performedwithin Matlab 2009, using the Genetic Algorithm Tool-box (The Mathworks 2009). TRNSYS is a dynamic sim-ulation tool developed over 30 years, with flexibility inmodeling systems and buildings. In TRNSYS, a modelis developed within the Simulation Studio environmenta graphical user modeling interface. The model is com-posed of ‘Types’, predefined components and algorithmswhich model the behavior of common systems. Themodel is saved in ASCII text format within in a ‘deck’file (.DCK), which stores the Types and connections be-tween them. The model is executed by TRNEXE, an al-gebraic and differential equation solver which iterativelycomputes the system state at each time step. Because themodel is stored in text format, the model can be parame-terized by a scripting languages such as Matlab.

Figure 3 shows a method of scripting the parameteri-

zation of the TRNSYS input .DCK file. First, the mod-eler creates the overall system model within the TRNSYSStudio, being careful to parameterize the model using textvariables and defining proper output files. A method ofstandardizing TRNSYS simulations has been described ina previous publication (Jones and Ledinger 2010). Themodeler then saves the model to the .DCK file. WithinMatlab, the .DCK is copied so as not to alter the origi-nal file. Parameters such as the weather file, the timestep,and convergence tolerances are then updated by searchingand replacing the relevant sections of the current .DCKfile. Next, Matlab searches and replaces the values forany design variables. The modeler should define the de-sign variables in a standard and clear manner such asGeneticVar1 = 10, GeneticVar2 = 0.75. The up-dated .DCK file is then executed by TRNEXE using thedos() command in Matlab, which is a simple command-line access function. The result files are parsed and loadedinto Matlab. Results are then post-processed to producethe final objective variables. With this method, a power-ful way of executing a model becomes available withinMatlab. For example, by simply executing the command;

Energy = evalTRNSYS([4 45 200], Vienna, 5)

the annual electrical energy demand of a solar domestichot water system is returned. In this case, the system hasa design vector defined by a collector area of 4m2 at 45◦ tothe horizontal, with a primary hot water storage tank vol-ume of 200L, and parameterized using the weather file forVienna at a 5 minute time step. Wrapping the executionof TRNSYS into a Matlab function provides the capabil-ity to evaluate thousands of candidate solutions with nouser quickly and easily.




Current

populationEvaluate fitness

Fitness

levelsOptimized?

Selection

Solution

ParentsRecombinationUnmutated

population Mutation

New

population

EndStart

Initial

population

1

2 3 4 5

6

78910

11

12

Yes

No

Figure 2: A generic genetic algorithm

Checkout (copy &

move)

.DCK

Template

Parameterize

Update Evaluation

Vector

Current .DCK

Execute .DCK

(TRNEXE.EXE)

Results files

Post-process

Answer

Figure 3: MATLAB - TRNSYS function wrapping




APPLICATIONSTwo problems were modeled, parameterized, and inter-

faced to the Matlab Genetic Algorithm Toolbox.

Simple buildingThe first model is based on the geometry of “Case 600”

of ASHRAE standard 140-2007, a standard defining testcases for building energy simulation programs (ASHRAE2007). The simple “shoebox” building seen in figure 4is optimized with the objective of minimizing the annualheating energy demand for Vienna weather conditions.The design variables, summarized in table 1, are the build-ing orientation, and the glazing area. A global search ofthe complete domain was performed, shown in figure 5.The first observation is that the design space is symmet-ric along the 180 ◦line, reducing the size of the searcharea by half. The glazing area influences the optimumorientation. If glazing is minimal (no windows), the ori-entation does not influence the optimum orientation. Thisis because no solar gains are possible to offset the heat-ing demand. If glazing is maximized, the south orienta-tion becomes the optimal, benefiting from solar heat gainsthrough the windows, and the north orientation is the leastoptimal. Therefore, the optimum solution to this designproblem is x1 = 0◦ (south) and x2 = 6m2, with a value of3549.0kWh/a heating energy (per annum).

Figure 4: Simple building geometry

Table 1: Design variables for simple buildingVariable Range Units Description

x1 0 ≤ x1 ≤ 180 ◦ Orientationx2 0 ≤ x2 ≤ 6 m2 Glazing area

This problem was modeled in TRNSYS and coupledto the Matlab genetic algorithm toolbox using the methoddescribed above. The default genetic algorithm toolbox

0

100

200

300

0

1

2

3

4

5

6

3400

3600

3800

4000

4200

4400

4600

4800

5000

5200

5400

An

nu

al

he

ati

ng

[k

Wh

]

Figure 5: Global search of design space

parameters, listed in table 2, were used.

Table 2: Matlab GA default parametersInitial population - 20Representation - DoubleFitness scaling - RankSelection - StochasticElite count - 2Crossover - ScatteredCrossover fraction - 0.8Stopping - Tolerance / GenerationsTolerance value - 0.5Stall size - 100Mutation - Adaptive Feasible

A common way to display the progress of a genetic al-gorithm optimization is to show the number of generationson the x-axis, and show the value of the objective functionon the y-axis. The best and worst solutions in the popu-lation at each generation are plotted, showing the rangeof solutions. The average population fitness is also plot-ted. Figure 6 shows the progress of the simple buildingdesign problem using the default algorithm. A commongenetic algorithm profile is seen, with a fast increase infitness in the first few generations, and smaller improve-ments throughout successive generations. These plots areuseful for analyzing an important quality of genetic algo-rithms; diversity. The diversity of the population is repre-sented by different metrics such as the spread of solutionsor the average distance between candidate solutions in thedesign space. In figure 5, diversity quickly decreases asall individuals in the population converge to the optimumof 3549.0kWh/a. Managing diversity involves ensuringthat diversity is high enough in order to properly searchthe design space, without being so high as to make the




number of function evaluations prohibitively high.

0 20 40 60 80 1003400

4100

4800

Generation

He

ati

ng

De

ma

nd

[k

Wh

]

Figure 6: Genetic algorithm progress

Solar domestic hot water systemA second design problem is presented in figure 7, a sim-

ple solar domestic hot water system. The system consistsof a collector field, a storage tank, and a differential con-troller with hysteresis. The design variables are presentedin table 3. The objective is to minimize the amount ofauxilliary electric resistive heating in the tank required tomeet the hot water demands of a family dwelling in Vi-enna.

Table 3: Design variables for simple solar systemVariable Range Units Description

x1 0 ≤ x1 ≤ 30 ◦C Lower differentialx2 0 ≤ x2 ≤ 30 ◦C Deadbandx3 0 ≤ x3 ≤ 90 ◦ Slopex4 0 ≤ x4 ≤ 0.500 m3 Volume

Because the design vector is four dimensional, a com-plete global search is computationally expensive. In-stead, a manual searches was performed, first overthe [x1, x2, 45, 0.300] plane, producing an optimum of1661.0kWh/a at [0, 0, 45, 0.300]. A second search over[0, 0, x3, x4] further optimized the design to 1657kWh/aat [0, 0, 45, 0.365]. Therefore, the expected optimum de-sign is to have a lower differential and hysteresis of 0◦C,a collector slope of 45◦, and a tank volume of 0.365m3.From a practical viewpoint, the controller settings are notrealistic since this causes pump cycling, but still providea suitable test for the optimization routine.

A number of runs were executed, summarized in table4. A simple investigation into the influence of populationsize on solution convergence was performed, with the first5 runs having a population of 5 individuals, and the next

Figure 7: Simple solar domestic hot water system

5 runs having a population of 20 individuals. In all runs100 generations were evaluated. In the case of the pop-ulation size of 20, better solutions are achieved, the bestbeing 1655.1kWh/a at [0, 0, 43.3, 0.369]. With a pop-ulation size of 5, the solutions are more varied, with anextreme case of 2489.7kWh/a at [12.3, 1.5, 39.6, 0.279].Clearly, the initial random population of 5 individualsdoes not provide enough initial diversity to ensure an opti-mum solution. One strategy with a low population size istherefore to increase the mutation rate to increase diversityover the generations. With 20 individuals, the chances arebetter that a random initial individual is close to the op-timum and able to propagate a better design throughoutthe generations. The cost of the larger population size isthe potential for increased number of evaluations. In thiscase, the run time was simply the average simulation timeof each individual multiplied by the population size, andmultiplied by the number of generations which was fixedat 100. A stopping criteria based on convergence wouldproduce a varying number of generations and a better in-dication of evaluation time cost.

CONCLUSIONS AND FUTURE WORKGenetic algorithms are powerful tools in finding so-

lutions to complex design problems. Their advantagesinclude parallelization, non-intuitive optimum solutions,and a robust search regardless of the smoothness of theunderlying function. Genetic algorithms are therefore in-teresting in the problem of sustainable building design.The simulation tool TRNSYS was coupled to the scriptingplatform Matlab with the Genetic Algorithm Toolbox. Bywrapping the simulation execution in a function, the mod-eler can execute TRNSYS thousands of times in an auto-mated fashion. Two simple case studies were used to showthe feasibility of using genetic algorithms in optimizingthe energy design of a simple building and a simple solar




Table 4: Run history for solar hot water systemR

un

Gen

erat

ions

Popu

latio

n

Tim

e[h

r]x1 x2 x3 x4 Best

1 100 5 3.3 2.1 0.6 44.1 0.419 1728.02 100 5 3.2 0.3 2.1 42.3 0.468 1665.53 100 5 3.0 0.0 9.3 44.1 0.351 1663.14 100 5 3.3 4.2 4.2 45.9 0.473 1864.25 100 5 3.6 12.3 1.5 39.6 0.279 2489.76 100 20 13.6 0.0 1.5 43.2 0.392 1657.17 100 20 13.7 0.0 0.0 43.3 0.369 1655.18 100 20 13.7 0.0 0.0 43.2 0.375 1656.29 100 20 14.0 0.0 0.7 43.4 0.360 1655.2

10 100 20 14.1 0.1 0.0 43.2 0.390 1655.2

domestic hot water system. In these case studies, the basicconcept was proven to be successful. Managing diversityand number of evaluations were determined to be criticalissues in designing a successful genetic algorithm for sus-tainable building design. In any optimization problem, theunderlying design space should be physically well under-stood by the modeler in order to identify symmetries andto evaluate candidate solutions from a practical perspec-tive.

This research will be continued to optimize the geneticalgorithm for sustainable building design, with the objec-tive of an optimization platform that is flexible enoughto handle any type of design vector for a multi-objectivesustainable building design problem. The parameters andmethod influencing population size and configuration, se-lection, mutation, and stopping criteria will be investi-gated to design a robust genetic algorithm. Such a plat-form well be beneficial in the design of the next genera-tion of sustainable buildings.

ACKNOWLEDGEMENTSThis work is developed within the doctoral program at

the Technical University of Vienna, Institute of ComputerTechnology under the supervision of Dr. Dietmar Diet-rich, and with support from the Austrian Institute of Tech-nology.

REFERENCESASHRAE. 2007. “Standard 140 - Standard Method of

Test for the Evaluation of Building Energy Analy-sis Computer Programs.” Technical Report, Amer-ican Society of Heating, Refrigerating and Air-Conditioning Engineers.

Bui, Lam Thu, and Semeer Alam. 2008. Multi-ObjectiveOptimization in Computational Intelligence. Infor-mation Science Reference, Hershey PA.

Eiben, A.E., and J.E. Smith. 2007. Introduction to Evo-lutionary Computing. Springer.

Jones, Marcus, and Stephan Ledinger. 2010. “Push-ing the limits of simulation complexity - a buildingenergy performance simulation of an exhibition cen-tre in the U.A.E.” Proceedings of Simbuild 2010 -4th national conference of IBPSA-USA (Pending fi-nal acceptance).

Keane, A.J., and S.M. Brown. 1996. “The design of asatellite boom with enhanced vibration performanceusing genetic algorithm techniques.” Proceedings ofthe Conference on Adaptive Computing in Engineer-ing Design and Control.

Solar Energy Laboratory, Univ. of Wisconsin-Madison.2009. TRNSYS 17 - a TRaNsient SYstem Simulationprogram.

The Mathworks. 2009. Matlab R2009b.

Wetter, Michael. 2004. “Simulation-Based Building En-ergy Optimization.” Ph.D. diss., University of Cali-fornia, Berkeley.




coupling trnsys and matlab for genetic algorithm ... · coupling trnsys and matlab for genetic...

Documents