This research focuses on the energy performance of office building in Wuhan. The research explored and predicted the optimal solution of design variables by Multi-Island Genetic Algorithm (MIGA) and RBF Artificial neural networks (RBF-ANNs). Research analyzed the cluster centers of design variable by K-means cluster method. In the study, the RBF-ANNs model was established by 1,000 simulation cases. The RMSE (root mean square error) of the RBF-ANNs model in different energy aspects does not exceed 15%. Comparing to the reference case (the largest energy consumption case in the optimization), the 214 elite cases in RBF-ANNs model save at least 37.5% energy. By the cluster centers of the design variables in the elite cases, the study summarized the benchmark of 14 design variables and also suggested a building energy guidance for Wuhan office building design.


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    Titel :

    Optimization and Prediction of Design Variables Driven by Building Energy Performance—A Case Study of Office Building in Wuhan


    Beteiligte:
    Yuan, Philip F. (Herausgeber:in) / Yao, Jiawei (Herausgeber:in) / Yan, Chao (Herausgeber:in) / Wang, Xiang (Herausgeber:in) / Leach, Neil (Herausgeber:in) / Li, Jingyi (Autor:in) / Chen, Hong (Autor:in)

    Kongress:

    The International Conference on Computational Design and Robotic Fabrication ; 2020 ; Shanghai, China July 05, 2020 - July 06, 2020


    Erschienen in:

    Erscheinungsdatum :

    29.01.2021


    Format / Umfang :

    14 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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