典型文献
Short-Term Electricity Price Forecasting Using Random Forest Model with Parameters Tuned by Grey Wolf Algorithm Optimization
文献摘要：
Accurately forecasting short-term electricity prices is of great significance to electricity market participants.Compared with the time series forecasting methods,machine learning forecasting methods can consider more external factors.The forecasting accuracy of machine learning models is greatly affected by the parameters,meanwhile,the manual selection of parameters usually cannot guarantee the accuracy and stability of the forecasting.Therefore,this paper proposes a random forest(RF)electricity price forecasting model based on the grey wolf optimizer(GWO)to improve the accuracy of forecasting.Among them,RF has a good ability to deal with the problem of non-linear and unstable electricity prices.The optimization of model parameters by GWO can overcome the instability of the forecasting accuracy of manually tune parameters.On this basis,the short-term electricity prices of the PJM power market in four seasons are separately predicted.Experimental results show that the RF algorithm can better predict the short-term electricity price,and the optimization of the RF forecasting model by GWO can effectively improve the accuracy of the RF forecasting model.
文献关键词：
中图分类号：
作者姓名：
Junshuang ZHANG;Ziqiang LEI;Runkun CHENG;Huiping ZHANG
作者机构：
State Key Laboratory of Electrical Insulation and Power Equipment,Xi'an Jiaotong University,Xi'an 710049,China;State Grid East Inner Mongolia Electric Power Co.,Ltd.,Hohhot 010010,China;Economics and Management School,North China Electric Power University,Beijing 102206,China
文献出处：
引用格式：
[1]Junshuang ZHANG;Ziqiang LEI;Runkun CHENG;Huiping ZHANG-.Short-Term Electricity Price Forecasting Using Random Forest Model with Parameters Tuned by Grey Wolf Algorithm Optimization)[J].系统科学与信息学报（英文版）,2022(02):167-180
A类：
B类：
Short,Term,Electricity,Price,Forecasting,Using,Random,Forest,Model,Parameters,Tuned,by,Grey,Wolf,Algorithm,Optimization,Accurately,forecasting,short,term,electricity,prices,significance,market,participants,Compared,series,methods,machine,learning,consider,more,external,factors,accuracy,models,greatly,affected,parameters,meanwhile,selection,usually,cannot,guarantee,Therefore,this,paper,proposes,random,forest,RF,grey,wolf,optimizer,GWO,improve,Among,them,has,good,deal,problem,linear,unstable,optimization,overcome,instability,manually,tune,On,basis,PJM,power,four,seasons,separately,predicted,Experimental,results,show,that,algorithm,better,effectively
AB值：
0.523244
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