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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    Title :

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


    Contributors:
    Yuan, Philip F. (editor) / Yao, Jiawei (editor) / Yan, Chao (editor) / Wang, Xiang (editor) / Leach, Neil (editor) / Li, Jingyi (author) / Chen, Hong (author)

    Conference:

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


    Published in:

    Publication date :

    2021-01-29


    Size :

    14 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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