Machine Learning Methods for Short-Term Electricity Price Prediction:Current Status,Challenges and Perspectives
With the gradual construction of the national unified electricity market,the field of electricity price forecasting has attracted more and more attention from scholars.The development of efficient and accurate short-term electricity price prediction models can not only provide decision support for buyers and sellers in the electricity spot market,but also enhance the risk control ability of all parties,thus providing quantitative support for the establishment of an efficient and unified electricity market.Due to the large randomness,high volatility and strong timeliness of electricity prices,the application of machine learning methods in the field of short-term electricity price prediction can take into account a variety of factors,and can fit the more complex nonlinear price data in a shorter period of time to obtain more accurate prediction results.This paper provides an overview of the short-term electricity price prediction methods based on machine learning methods,analyzes the prediction effect and applicability of each type of method in different contexts and their advantages and shortcomings,and and looks forward to the future research direction.
NETL
NSTL
万方数据
