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An Ensemble Wavelet Deep Learning Approach for Short-term Load Forecasting

Short-term load forecasting can provide essential and informative inputs for various decision-making problems, such as optimal operation and control of power systems. In this paper, a novel ensemble wavelet deep learning approach is proposed for short-term load forecasting. Firstly, electricity load series are decomposed into one lower frequency component and three higher frequency components using wavelet decomposition. Then, the forecasting values of each component can be obtained by the ensemble deep learning model consisting of deep belief network, long short-term memory and multi-layer perceptron. Finally, the forecasting result of the proposed model is calculated by summing up the four components’ forecasting values. Numerical computational results have demonstrated the effectiveness of proposed forecasting model.