Comparison of four algorithms based on machine learning for cooling load forecasting of large-scale shopping mall

Short-term forecasting of air-conditioning cooling load of shopping mall is hard with much accuracy due to its chaotic and non-linear characteristic. Four forecasting algorithms based on machine learning are illustrated in this paper including Chaos-SVR, WD-SVR, SVR and BP, whose predicting performances are compared. For Chaos-SVR, the selection of lag time and embedding dimension during phase space reconstruction are described, while for WD-SVR, the modeling process of DB2 is proposed. Furthermore, the optimization of the hyper-parameters for SVR model is also presented. It's shown that these four approaches have different characteristics which are suitable for different types of cooling load time series. (C) 2017 The Authors. Published by Elsevier Ltd.