Predicting China’s energy consumption: Combining machine learning with three-layer decomposition approach

Abstract

Accurately predicting energy consumption (EC) is a difficult task, owing to its inherent complexity and nonlinearity features. To decrease the complexity of EC predictions, applying a novel three-layer decomposition approach to decompose a complex series into a trend sub-series and several simpler non-trend sub-series. Since the trend sub-series describes the developing tendency that is determined by the primarily influencing factors on EC, it is predicted by multivariate linear regression (MLR). In view of its learning ability of nonlinear mappings and excellent performance in dealing with long term dependency, long short-term memory neural network (LSTM) is applied to predict non-trend sub-series. Then, the final EC prediction is solved by calculating the sum of all these sub-series prediction values. A novel EC forecasting model based on machine learning with three-layer decomposition-ensemble approach is proposed in this study. Compared with traditional decomposition approaches (trend decomposition (TD), wavelet decomposition (WD), empirical mode decomposition (EMD), TD-EMD and TD-WD), the proposed three-layer decomposition approach can improve the prediction performance by combining the advantages of TD, EMD and WD. The proposed forecasting model is validated by empirical studies using the EC data of China and U.S. Finally, this study gives the China’s EC prediction in 2020–2024.