Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology

Abstract

The forecasting of electricity price remains the subject of increasingly intense research attention as the market structure becomes more complicated with the deregulation waves and the increasing level of price fluctuations observed. The heterogeneous data structure revealed in the recent empirical studies serves as the important stylized fact to be explored and analyzed in the heterogeneous market structure framework. Facing the increasingly diversified and more integrated market environment, the forecasting model in the electricity markets needs to take into account the individual and inter dependent heterogeneity features such as noises. In this paper, under the proposed HMH (Heterogeneous Market Hypothesis), we propose a BED (Bivariate EMD Denoising) based forecasting methodology to track and predict the electricity price movement. The BED algorithm is introduced as the feature extraction tool to identify and remove the noises, where the Error Entropy is further used as the criteria to determine the optimal level in EMD (Empirical Mode Decomposition) to be shrinkaged. Empirical studies conducted in the Australian electricity markets demonstrate the significant performance improvement of the proposed BED algorithm incorporating the heterogeneous market characteristics, against benchmark models.