Optimization of building energy consumption using single and multi-objective particle swarm optimization and genetics algorithms

Since significant energy consumption worldwide is related to building energy, building energy optimization is currently very important despite the limitations and due to the preservation of the earth's environment. This paper addresses the important limitations for optimizing building energy performance using single and multi-objective particle swarm optimization (MOPSO) and genetics (NSGA-II) algorithms with EnergyPlus and MATLAB building energy simulation software to examine different weather data of several cities in Iran with considering the one-room model offers with the effect of building architectural parameters. Also in the optimization section, the annual electricity consumption of cooling, heating, and lighting to understand the interactions between the target functions and minimize the total annual energy demand of the building is examined, and the results obtained, the annual cooling electricity consumption is reduced by 19.8-33.3% and the annual heating and lighting Respectively increased by 1.7-4.8% and 0.5-2.6% compared to other models, which leads to an optimal reduction of 1.6-11.3% of the total annual electricity demand of the building.