Application of artificial intelligence technology in real-time electricity price forecasting: window-based XGBoost

Abstract

Building a new power system is an important measure taken by China to cope with climate change problems. Establishing a flexible and perfect electricity market and price mechanism is an important means to ensure the safe and stable operation of the power system. As one of the important electricity price mechanisms, the change in real-time electricity price (RTP) is a key factor in the operation of the electricity market, and each market participant can formulate a response strategy according to the RTP. However, the uncertain, stochastic, and fluctuant characteristics are definitely difficult problems for the RTP prediction. With the aim of solving this issue, this paper proposed a RTP prediction method based on a window-based XGBoost model. Through the input conversion of the proposed model, it can help to reduce the complexity and capture the autocorrelation effect of the RTP. The case study is conducted through the actual load data of a province in China and the superiority is proved by comparing with several state-of-art models. The result shows that the window-based XGBoost model applied in this paper can decrease the prediction error by 69.48%-95.67% and greatly enhance RTP's prediction performance.