Electricity Market Price Forecasting for a High Renewable Penetrated Power System via Random Forest

Abstract

The Electricity Price Forecasting (EPF) is essential to the bidding strategy formulation and the market operation. However, lack of data and volatility of power generation have put forward new challenges for EPF. To address this problem, we propose an online self-adaptive forecasting method based on random forests. Our approach takes possible fluctuations of the market into consideration, and adapts to them by maintaining training sets of different size. A case study using actual electricity market data has shown that our proposed approach obtains higher accuracy as well as sheds light on possible concept drift in the market.