Cumincad : CUMINCAD Papers : Paper cdrf2019_229:Optimization and Prediction of Design Variables Driven by Building Energy Performance

id	cdrf2019_229
authors	Jingyi Li and Hong Chen
year	2020
title	Optimization and Prediction of Design Variables Driven by Building Energy Performance—A Case Study of Office Building in Wuhan
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_22
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	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.
series	cdrf
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last changed	2022/09/29 07:51