Multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms

While optimization studies focusing on real-world buildings are somewhat limited, many building optimization studies to date have used simple hypothetical buildings for the following three reasons: (1) the shape and form of real buildings are complex and difficult to mathematically describe; (2) com...

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Vydáno v:	Advanced engineering informatics Ročník 40; s. 93 - 109
Hlavní autoři:	Si, Binghui, Wang, Jianguo, Yao, Xinyue, Shi, Xing, Jin, Xing, Zhou, Xin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.04.2019
Témata:	Artificial neural network Building design optimization Multi-objective optimization algorithms Performance evaluation of algorithms Real-world building design Building design optimization Artificial neural network Multi-objective optimization algorithms Performance evaluation of algorithms Real-world building design
ISSN:	1474-0346
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Popis
Shrnutí:	While optimization studies focusing on real-world buildings are somewhat limited, many building optimization studies to date have used simple hypothetical buildings for the following three reasons: (1) the shape and form of real buildings are complex and difficult to mathematically describe; (2) computer models built based on real buildings are computationally expensive, which makes the optimization process time-consuming and impractical and (3) although algorithm performance is crucial for achieving effective building performance optimization (BPO), there is a lack of agreement regarding the proper selection of optimization algorithms and algorithm control parameters. This study applied BPO to the design of a newly built complex building. A number of design variables, including the shape of the building’s eaves, were optimized to improve building energy efficiency and indoor thermal comfort. Instead of using a detailed simulation model, a surrogate model developed by an artificial neural network (ANN) was used to reduce the computing time. In this study, the performance of four multi-objective algorithms was evaluated by using the proposed performance evaluation criteria to select the best algorithm and parameter values for population size and number of generations. The performance evaluation results of the algorithms implied that NSGA-II (with a population size and number of generations of 40 and 45, respectively) performed the best in the case study. The final optimal solution significantly improves building performance, demonstrating the success of the BPO technique in solving complex building design problems. In addition, the findings on the performance evaluation of the algorithms provide guidance for users regarding the selection of suitable algorithms and parameter settings based on the most important performance criteria.
ISSN:	1474-0346
DOI:	10.1016/j.aei.2019.03.006