Data-driven Two-step Day-ahead Electricity Price Forecasting Considering Price Spikes  

作　　者：Shengyuan Liu Yicheng Jiang Zhenzhi Lin Fushuan Wen Yi Ding Li Yang 

机构地区：[1]School of Electrical Engineering,Zhejiang University,Hangzhou 310027,China [2]School of Electrical Engineering,Shandong University,Jinan 250061,China [3]State Grid Zhejiang Electric Power Corporation,Hangzhou 310007,China,Hangzhou 310027,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2023年第2期523-533,共11页现代电力系统与清洁能源学报（英文）

基　　金：supported by National Natural Science Foundation of China (No.52077195);Zhejiang University Academic Award for Outstanding Doctoral Candidates (No.202022)。

摘　　要：In the electricity market environment,electricity price forecasting plays an essential role in the decision-making process of a power generation company,especially in developing the optimal bidding strategy for maximizing revenues.Hence,it is necessary for a power generation company to develop an accurate electricity price forecasting algorithm.Given this background,this paper proposes a two-step day-ahead electricity price forecasting algorithm based on the weighted Knearest neighborhood(WKNN)method and the Gaussian process regression(GPR)approach.In the first step,several predictors,i.e.,operation indicators,are presented and the WKNN method is employed to detect the day-ahead price spike based on these indicators.In the second step,the outputs of the first step are regarded as a new predictor,and it is utilized together with the operation indicators to accurately forecast the electricity price based on the GPR approach.The proposed algorithm is verified by actual market data in Pennsylvania-New JerseyMaryland Interconnection(PJM),and comparisons between this algorithm and existing ones are also made to demonstrate the effectiveness of the proposed algorithm.Simulation results show that the proposed algorithm can attain accurate price forecasting results even with several price spikes in historical electricity price data.

关 键 词：Electricity market electricity price forecasting price spike weighted K-nearest neighborhood(WKNN) Gaussian process regression(GPR). 

分 类 号：TM73[电气工程—电力系统及自动化]