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Surrogate Based Multi-objective Optimization for Energy-Saving Building Design
https://orcid.org/http://orcid.org/0000-0003-2668-3578
https://orcid.org/http://orcid.org/0000-0002-3012-7913
In order to coordinate building energy consumption and indoor comfort for architectural design, a surrogate based multi-objective optimization approach is proposed in this paper. Taking a rectangular planar layout of civil housing as an example, several design parameters closely related to building energy consumption and indoor comfort are selected. With the help of EnergyPlus energy consumption simulation analysis software, the sample data can be generated by simulating the software. Thus, a three-layer radial basis function (RBF) neural network is used to train and learn the sample data, and the models of building energy consumption and indoor comfort can be established. The established energy consumption and comfort models are verified by the experiments. On this basis, NSGA-II multi-objective optimization algorithm is used to balance the building energy consumption and indoor comfort to obtain the value range of design parameters and the Pareto front. So, the optimization results can be used to guide energy saving architectural design.
Surrogate Based Multi-objective Optimization for Energy-Saving Building Design
https://orcid.org/http://orcid.org/0000-0003-2668-3578
https://orcid.org/http://orcid.org/0000-0002-3012-7913
In order to coordinate building energy consumption and indoor comfort for architectural design, a surrogate based multi-objective optimization approach is proposed in this paper. Taking a rectangular planar layout of civil housing as an example, several design parameters closely related to building energy consumption and indoor comfort are selected. With the help of EnergyPlus energy consumption simulation analysis software, the sample data can be generated by simulating the software. Thus, a three-layer radial basis function (RBF) neural network is used to train and learn the sample data, and the models of building energy consumption and indoor comfort can be established. The established energy consumption and comfort models are verified by the experiments. On this basis, NSGA-II multi-objective optimization algorithm is used to balance the building energy consumption and indoor comfort to obtain the value range of design parameters and the Pareto front. So, the optimization results can be used to guide energy saving architectural design.
Surrogate Based Multi-objective Optimization for Energy-Saving Building Design
https://orcid.org/http://orcid.org/0000-0003-2668-3578
https://orcid.org/http://orcid.org/0000-0002-3012-7913
In order to coordinate building energy consumption and indoor comfort for architectural design, a surrogate based multi-objective optimization approach is proposed in this paper. Taking a rectangular planar layout of civil housing as an example, several design parameters closely related to building energy consumption and indoor comfort are selected. With the help of EnergyPlus energy consumption simulation analysis software, the sample data can be generated by simulating the software. Thus, a three-layer radial basis function (RBF) neural network is used to train and learn the sample data, and the models of building energy consumption and indoor comfort can be established. The established energy consumption and comfort models are verified by the experiments. On this basis, NSGA-II multi-objective optimization algorithm is used to balance the building energy consumption and indoor comfort to obtain the value range of design parameters and the Pareto front. So, the optimization results can be used to guide energy saving architectural design.
Surrogate Based Multi-objective Optimization for Energy-Saving Building Design
Lecture Notes in Civil Engineering
Guo, Wei (editor) / Qian, Kai (editor) / Chen, Yuchen (author) / Shi, Xuhua (author)
International Conference on Green Building, Civil Engineering and Smart City ; 2022 ; Guilin, China
2022-09-08
8 pages
Article/Chapter (Book)
Electronic Resource
English
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