A comprehensive review and analysis of solar forecasting techniques

查看全文 作　　者：Pardeep [1]SINGLA;Manoj [1]DUHAN;Sumit [2]SAROHA

机构地区：[1]Department of ECE,Deenbandhu Chhotu Ram University of Science and Technology,Murthal,Sonepat 131039,India;[2]Department of Printing Technology(Electrical Engineering),Guru Jambheshwar University of Science and Technology,Hisar 125001,India

出　　处：《Frontiers in Energy》索引2022年第16卷第2期,共37页

摘　　要：In the last two decades,renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic,industrial,and agriculture sectors.Solar forecasting plays a vital role in smooth operation,scheduling,and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants.Numerous models and techniques have been developed in short,mid and long-term solar forecasting.This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature,by mainly focusing on investigating the influence of meteorological variables,time horizon,climatic zone,pre-processing techniques,air pollution,and sample size on the complexity and accuracy of the model.To make the paper reader-friendly,it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication,time resolution,input parameters,forecasted parameters,error metrics,and performance.The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities.Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data.Besides,it also discusses the diverse key constituents that affect the accuracy of a model.It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model. 关键词：forecasting techniques hybrid models neural network solar forecasting error metric support vector machine(SVM)

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A comprehensive review and analysis of solar forecasting techniques

参考文献(170)

引证文献(4)

耦合文献(184)

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