Heat load prediction of residential buildings based on discrete wavelet transform and tree-based ensemble learning

Abstract

Currently, the high energy consumption of district heating system represents a problem. Thus, accurate prediction of future heat load is the key to ensure energy saving and improve system operation efficiency. However, the accuracy of the prediction model based on single machine learning algorithms need to be improved, and the hybrid prediction model suffers from redundant inputs. The objective of this study was to obtain the most accurate prediction of the short-term heat load consumption. The discrete wavelet transform, two tree-based ensemble learning algorithms, extremely randomized trees regression and gradient boosting decision tree were used to establish hybrid models for predicting the heat load 1 h in advance. Pearson and least absolute shrinkage and selection operator (LASSO) methods were used to optimize the feature set, including system parameters, meteorological parameters and time steps. This resulted in four feature sets. In addition, the effect of the historical heat load on the performance of the prediction model was analysed. Actual operation data of an established heating system were collected to test the performance of the models. The results were then compared with those of the models established by artificial neural network and support vector regression. The results show that the historical heat load had a significant impact on the prediction accuracy of the heat load of the upcoming hour, and the extreme random tree model based on discrete wavelet transform showed a better prediction performance and lower model complexity in case of using the feature set selected by the LASSO method.