An Online Electricity Market Price Forecasting Method Via Random Forest

Abstract

Electricity price forecasting (EPF) is essential to the bidding strategy formulation and market operation. Since EPF is important in the electricity market, lots of forecasting approaches are proposed. However, the new scene caused by the volatility of renewable power generation and other volatility factors has made previous methods inaccurate and inapplicable. To address this problem, we propose an online self-adaptive forecasting method based on random forest, which is different from the traditional batch learning. Our approach takes possible fluctuations of the market into consideration, and adapts to them by maintaining training sets of different sizes. A case study using actual electricity market data has shown that our proposed approach obtains higher accuracy than ordinary approaches, as well as sheds light on possible concept drift in the market.