ANFIS-Based Modeling for Photovoltaic Characteristics Estimation
1
School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
2
Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
*
Author to whom correspondence should be addressed.
Academic Editors: Ka Lok Man, Yo-Sub Han and Hai-Ning Liang
Symmetry 2016, 8(9), 96; https://doi.org/10.3390/sym8090096
Received: 30 July 2016 / Revised: 10 September 2016 / Accepted: 12 September 2016 / Published: 16 September 2016
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
Due to the high cost of photovoltaic (PV) modules, an accurate performance estimation method is significantly valuable for studying the electrical characteristics of PV generation systems. Conventional analytical PV models are usually composed by nonlinear exponential functions and a good number of unknown parameters must be identified before using. In this paper, an adaptive-network-based fuzzy inference system (ANFIS) based modeling method is proposed to predict the current-voltage characteristics of PV modules. The effectiveness of the proposed modeling method is evaluated through comparison with Villalva’s model, radial basis function neural networks (RBFNN) based model and support vector regression (SVR) based model. Simulation and experimental results confirm both the feasibility and the effectiveness of the proposed method.
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Keywords:
ANFIS; modeling; characteristic estimation; photovoltaic module
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MDPI and ACS Style
Bi, Z.; Ma, J.; Pan, X.; Wang, J.; Shi, Y. ANFIS-Based Modeling for Photovoltaic Characteristics Estimation. Symmetry 2016, 8, 96. https://doi.org/10.3390/sym8090096
AMA Style
Bi Z, Ma J, Pan X, Wang J, Shi Y. ANFIS-Based Modeling for Photovoltaic Characteristics Estimation. Symmetry. 2016; 8(9):96. https://doi.org/10.3390/sym8090096
Chicago/Turabian StyleBi, Ziqiang; Ma, Jieming; Pan, Xinyu; Wang, Jian; Shi, Yu. 2016. "ANFIS-Based Modeling for Photovoltaic Characteristics Estimation" Symmetry 8, no. 9: 96. https://doi.org/10.3390/sym8090096
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