Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods

Abstract

In the context of developing a unified national electricity market， the development of a spot market helps promote the sharing and optimal allocation of electricity resources on a larger scale. As important decision-making information for market participants， spot electricity prices are crucial for auxiliary decision-making in the spot market， market operation monitoring， and risk management. The rapid development of machine learning methods provided a feasible approach for electricity price prediction. This study first analyzed the characteristics of spot electricity prices and their influence on the unified national electricity market. The types of prediction models and challenges faced by spot electricity price prediction can be elaborated based on existing research on electricity price prediction mechanisms. In addition， based on the characteristics of data labeling， feature extraction and data flow control， the research status of various machine learning prediction models was summarized， and the characteristics and applicability of different prediction models were analyzed. This study then analyzed the evaluation criteria for spot electricity price prediction models based on machine learning， and summarized the model hyperparameter training requirements and the practical application of relevant prediction methods. Finally， in view of the challenges of machine learning methods in electricity price prediction research， this study outlined future research directions to provide constructive references for the development of the spot market under the construction of a unified national electricity market.

Keywords

• national unified electricity market|spot market|electricity price forecasting|machine learning methods