电力建设2025，Vol.46Issue(2)：160-179,20.DOI:10.12204/j.issn.1000-7229.2025.02.014
基于机器学习方法的现货电价预测研究综述
Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods
摘要
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.关键词
全国统一电力市场/现货市场/电价预测/机器学习方法Key words
national unified electricity market/spot market/electricity price forecasting/machine learning methods分类
信息技术与安全科学引用本文复制引用
加鹤萍,郭宇辰,马乾鑫,杨争林,郑亚先,曾丹,刘敦楠..基于机器学习方法的现货电价预测研究综述[J].电力建设,2025,46(2):160-179,20.基金项目
This work is supported by National Key R&D Program of China(No.2022YFB2403000),Beijing Social Science Foundation(No.24JCC101),and Beijing Natural Science Foundation(No.9242015). 国家重点研发计划项目(2022YFB2403000) （No.2022YFB2403000）
北京市社会科学基金决策咨询项目(24JCC101) （24JCC101）
北京市自然科学基金面上项目(9242015) （9242015）
