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Proceedings of CAD'17, 2017, 362-366
Use of Neural Network Supervised Learning to Enhance the Light Environment Adaptation Ability and Validity of Green BIM

Shang-yuan Chen, Feng Chia University

Abstract. Green building information modeling (Green BIM) integrated design and analysis procedures have become an important tool for architects and design teams wishing to select and improve design proposals. Nevertheless, when using building performance analysis (BPA) software to predict building performance in actual environments, there are inevitably discrepancies between simulation data obtained from the software and measurements in the actual environment, which has caused the software's simulation performance validity to be questioned. This project therefore seeks to use supervised learning by a neural network to reduce this gap, and enhance the optimization ability of Green BIM.

Keywords. Green BIM, neural network supervised learning, CNS illuminance standards

DOI: 10.14733/cadconfP.2017.362-366