Estimating photovoltaic power generation: Performance analysis of artificial neural networks, Support Vector Machine and Kalman filter

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• 作者：Raul V.A. Monteiro^a ; ^{raulvitoramonteiro@gmail.com} ; ^{rauldvm@terra.com.br} ; Geraldo C. Guimarã ; es^a ; ^{gcaixetag@gmail.com} ; Fabricio A.M. Moura^b ; ^{fabricio@eletrica.uftm.edu.br} ; Madeleine R.M.C. Albertini^b ; ^{rociodelpilar_21@hotmail.com} ; Marcelo K. Albertini^c ; ^{albertini@ufu.br}
• 关键词：Training algorithms ; Artificial neural network ; Support Vector Machine ; Kalman filter ; Photovoltaic ; Power generation
• 刊名：Electric Power Systems Research
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：143
• 期：Complete
• 页码：643-656
• 全文大小：4037 K
• 卷排序：143

文摘

The comparison of 7 training algorithms from artificial neural networks used for estimating photovoltaic active power generation. Photovoltaic active power generation estimating using the Support Vector Machine technique. Photovoltaic active power generation estimating using the time-varying Kalman filter technique. Performance analysis made between artificial neural networks, Support Vector Machine and Kalman filter for photovoltaic active power generation estimating.