A Hybrid Model for Short-term PV Output Forecasting Based on PCA-GWO-GRNN

作     者：Leijiao Ge Yiming Xian Jun Yan Bo Wang Zhongguan Wang Leijiao Ge;Yiming Xian;Jun Yan;Bo Wang;Zhongguan Wang

作者机构：the Key Laboratory of Smart Grid of Ministry of EducationTianjin UniversityTianjin 300072China the State Key Laboratory of Reliability and Intelligence of Electrical EquipmentHebei University of TechnologyTianjin 300132China the Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealQC H3G 1M8Canada the School of Electrical and AutomationWuhan UniversityWuhan 430072China 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2020年第8卷第6期

页      面：1268-1275页

核心收录：

学科分类：12[管理学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0807[工学-动力工程及工程热物理] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Key Research and Development Program of China(No.2018YFB1500800) the National Natural Science Foundation of China(No.51807134) 

主　　题：Photovoltaic output forecasting principal component analysis(PCA) grey wolf optimization(GWO) generalized regression neural network(GRNN) 

摘      要：High-precision day-ahead short-term photovoltaic(PV)output forecasting is essential in PV integration to the smart distribution networks and multi-energy system,and provides the foundation for the security,stability,and economic operation of PV *** paper proposes a hybrid model based on principal component analysis,grey wolf optimization and generalized regression neural network(PCA-GWO-GRNN)for day-ahead short-term PV output forecasting,considering the features of multiple influencing factors and strong *** paper first uses the PCA to reduce the dimension of meteorological ***,the high-precision day-ahead short-term PV output forecasting based on GWO-GRNN model is *** is used to regressively analyze the input features after dimension reduction,and the parameter of GRNN is optimized by using GWO,which has strong global searching ability and fast *** proposed PCA-GWO-GRNN model effectively achieves a high precision in day-ahead shortterm PV output forecasting,which is demonstrated in a case study on a real PV plant in Jiangsu province,*** results have validated the accuracy and applicability of the proposed model in real scenarios.