Forecasting performance comparison of two hybrid machine learning models for cooling load of a large-scale commercial building

Abstract

The hourly cooling load forecasting of a commercial building is very hard to be guaranteed with high accuracy. Due to the high cost of purchase, installation and maintenance for data acquisition devices in some cases, the forecasting method for univariate time series with nonlinear, random, large fluctuation is investigated in this paper and two hybrid machine learning modelling methods – Chaos–support vector regression (Chaos-SVR) and wavelet decomposition (WD) – SVR are presented. The optimization methods of the lag time and embedding dimension during reconstruction of the phase space are described in detail for Chaos–SVR while the selection processes for the wavelet-based function are also presented for wavelet decomposition –SVR. The prediction accuracy of these two hybrid forecasting methods are compared with backward propagation and SVR according to various evaluation metrics such as the expected error percentage(EEP), mean bias error(MBR), coefficient of variation of root mean square deviation(CV-RMSE), mean absolute percentage error (MAPE) and R2 determination coefficient. The forecasting results show that the Chaos–SVR modelling method outperforms the WD-SVR while EEP of Chaos-SVR is 7.4% better than WD-SVR– because of the chaotic characteristics of the cooling load for commercial buildings, and both of the hybrid algorithms outperform the single prediction algorithms, BP and SVR whose EEPs are at least 60% better than that of the other single ones. Although hybrid machine learning algorithms involve more complex modelling procedures than single prediction algorithms, their prediction times are still less than the single ones. The hybrid forecasting methods proposed in this work could also be used in other forecasting fields.