A hybrid electricity price forecasting model with Bayesian optimization for German energy exchange

Abstract

Electricity price forecasting affects the operation of the entire electricity market and it is extremely important to every market participant. In this paper, a novel hybrid method, with using empirical wavelet transform (EWT), support vector regression (SVR), Bi-directional long short-term memory (BiLSTM) and Bayesian optimization (BO), is proposed to increase the accuracy of electricity price forecasting. First, EWT is used as a processing tool to decompose the original signal into specific modal components according to the characteristics of the signal itself. Then, considering the complexity of forecasting nonlinear subseries, SVR and BiLSTM are used as basic framework to forecast the nonlinear subseries. At the same time, BO is introduced to adjust parameters and optimize model performance. In last, the final prediction results are combined by the prediction results of different models. The proposed hybrid model is employed on the data gathered from the European Power Exchange Spot (EPEXSPOT). Five different case studies are adopted to verify the effectiveness of BO, EWT and hybrid model respectively. Statistical tests of experimental results compared with other situations demonstrated the proposed hybrid model can achieve a better forecasting performance.