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arXiv · 1801.00779

Machine Learning for Building Energy and Indoor Environment: A Perspective

Zhijian Liu·Di Wu·Hongyu Wei·Guoqing Cao
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Abstract

Machine learning is a promising technique for many practical applications. In this perspective, we illustrate the development and application for machine learning. It is indicated that the theories and applications of machine learning method in the field of energy conservation and indoor environment are not mature, due to the difficulty of the determination for model structure with better prediction. In order to significantly contribute to the problems, we utilize the ANN model to predict the indoor culturable fungi concentration, which achieves the better accuracy and convenience. The proposal of hybrid method is further expand the application fields of machine learning method. Further, ANN model based on HTS was successfully applied for the optimization of building energy system. We hope that this novel method could capture more attention from investigators via our introduction and perspective, due to its potential development with accuracy and reliability. However, its feasibility in other fields needs to be promoted further.

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Zhijian Liu, Di Wu, Hongyu Wei, Guoqing Cao. 2017-12-31. Machine Learning for Building Energy and Indoor Environment: A Perspective. https://arxiv.org/abs/1801.00779

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