Multi-objective optimization of building energy performance using GA coupled with EnergyPlus: A case study in various climatic zones of Iran

Khadijeh Azarbad¹ Navid Delgarm² Zahra Zarei Ardestani³

Abstract :
In this paper a multi-objective genetic algorithm is coupled to a whole building energy simulation program to obtain the optimal solutions in order to promote the building energy performance. The scope of this study is to explore the effect of some architectural parameters including building orientation, window size and overhang specifications on the energy performance of building envelope in four climates of Iran. In the optimization section, multi-criteria optimization analyses of total annual cooling load and total annual lighting load are investigated with the aim of understanding the interactions between two mentioned objective functions. The optimal solutions from the multi-objective optimization problem are reported in the form of Pareto frontiers and finally the results of the multi-criteria optimization are compared with the EnergyPlus baseline model. The results show that for a typical model, the total annual cooling load may be reduced about 34.6 to 63.3 % by changing its specifications and increasing the total annual lighting load by about 0.79 to 2.4 %. It means that the architectural design parameters of building are important and critical in determining building energy consumption.

Keywords : energyplus jeplus genetic algorithms energy consumption multi objective optimization

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زبان مقاله : انگلیسی
محور مقاله : علوم مهندسی
محل انتشار : سومین کنگره بین المللی علوم، مهندسی و تکنولوژی - هامبورگ
آدرس وب سایت کنفرانس : germanconf.com
کد IOI مقاله : XCZE-ECFHF
لینک مستقیم مقاله : https://isnac.ir/XCZE-ECFHF

سایر مقالات ارائه دهنده

1. Simulation–based multi-criteria optimization of the building energy consumption using NSGA-II
2. A comparison of the roll of window size and glazing type in different building orientations to reduce energy consumption
3. Multi-objective optimization of building energy performance using GA coupled with EnergyPlus: A case study in various climatic zones of Iran

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