An effective Two-Stage Electricity Price forecasting scheme

Abstract

With the development of the global power market reform, the monopoly of the power sector and government control pattern has gradually broken. Due to the unique properties of electricity, electricity prices show high volatility and uncertainty, bringing significant challenges to the accurate prediction of electricity prices. The sudden occurrence of a few spike prices in the electricity spot market has significantly affected electricity price forecasting accuracy. We propose a novel two-stage electricity price forecasting scheme (TSEP). A multi-source data-based spike occurrence prediction scheme is presented in the first stage, which adopts a deep neural network (DNN) to predict whether the price to be forecasted is a spike or not. Specifically, to alleviate the impact of low spike price samples, the oversampling method is used to synthesize some spikes at the data level. A loss function with a misclassification penalty to increase the cost of missing price spikes is designed at the algorithm level. Based on the outputs of the first stage, in the second stage, TSEP exploits the variance stabilizing transformations respectively suitable for pre-processing spike and normal prices and combines an artificial neural network (ANN) based spike calibration model to improve the accuracy of electricity price forecasting further. The experimental results on the European Power Exchange for France (EPEX-FR) demonstrate that TSEP increases spike occurrence prediction accuracy compared with the conventional models and significantly improves the accuracy of spike electricity price forecasting without affecting the accuracy of forecasting normal electricity price.