Modeling and simulation of PV modules based on ANFIS

Ziqiang Bi; Jieming Ma; Wanjun Hao; Xinyu Pan; Jian Wang; Jianmin Ban; Ka Lok Man

doi:10.1007/978-981-10-1536-6_48

Modeling and simulation of PV modules based on ANFIS

Ziqiang Bi, Jieming Ma^*, Wanjun Hao, Xinyu Pan, Jian Wang, Jianmin Ban, Ka Lok Man

^*Corresponding author for this work

Department of Computing

Suzhou University of Science and Technology

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

This work presents an optimized method to simulate the modeling of photovoltaic (PV) modules with measured data of PV array. The current-voltage (I-V) characteristics are estimated via adaptive neuro-fuzzy inference system (ANFIS). The proposed ANFIS method takes advantages of no need of internal parameters of PV model and can achieve a more accurate estimation of PV characteristics. By compared with Villalva’s model, radial basis function neural networks (RBFNN) and support vector machine (SVM) method, the results predicted by the proposed ANFIS approach show the best estimation performance in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R²).

Original language	English
Title of host publication	Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE
Editors	Hai Jin, Young-Sik Jeong, Muhammad Khurram Khan, James J. Park
Publisher	Springer Verlag
Pages	365-371
Number of pages	7
ISBN (Print)	9789811015359
DOIs	https://doi.org/10.1007/978-981-10-1536-6_48
Publication status	Published - 2016
Event	11th International Conference on Future Information Technology, FutureTech 2016 - Beijing, China Duration: 20 Apr 2016 → 22 Apr 2016

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	393
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	11th International Conference on Future Information Technology, FutureTech 2016
Country/Territory	China
City	Beijing
Period	20/04/16 → 22/04/16

Keywords

ANFIS
Characteristic estimation
Modeling

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-10-1536-6_48

Cite this

Bi, Z., Ma, J., Hao, W., Pan, X., Wang, J., Ban, J., & Man, K. L. (2016). Modeling and simulation of PV modules based on ANFIS. In H. Jin, Y.-S. Jeong, M. K. Khan, & J. J. Park (Eds.), Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE (pp. 365-371). (Lecture Notes in Electrical Engineering; Vol. 393). Springer Verlag. https://doi.org/10.1007/978-981-10-1536-6_48

@inproceedings{f9abcaf2b7e144bfa388004bdefaec9f,

title = "Modeling and simulation of PV modules based on ANFIS",

abstract = "This work presents an optimized method to simulate the modeling of photovoltaic (PV) modules with measured data of PV array. The current-voltage (I-V) characteristics are estimated via adaptive neuro-fuzzy inference system (ANFIS). The proposed ANFIS method takes advantages of no need of internal parameters of PV model and can achieve a more accurate estimation of PV characteristics. By compared with Villalva{\textquoteright}s model, radial basis function neural networks (RBFNN) and support vector machine (SVM) method, the results predicted by the proposed ANFIS approach show the best estimation performance in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2).",

keywords = "ANFIS, Characteristic estimation, Modeling",

author = "Ziqiang Bi and Jieming Ma and Wanjun Hao and Xinyu Pan and Jian Wang and Jianmin Ban and Man, {Ka Lok}",

note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2016.; 11th International Conference on Future Information Technology, FutureTech 2016 ; Conference date: 20-04-2016 Through 22-04-2016",

year = "2016",

doi = "10.1007/978-981-10-1536-6_48",

language = "English",

isbn = "9789811015359",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "365--371",

editor = "Hai Jin and Young-Sik Jeong and Khan, {Muhammad Khurram} and Park, {James J.}",

booktitle = "Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE",

}

Bi, Z, Ma, J, Hao, W, Pan, X, Wang, J, Ban, J & Man, KL 2016, Modeling and simulation of PV modules based on ANFIS. in H Jin, Y-S Jeong, MK Khan & JJ Park (eds), Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE. Lecture Notes in Electrical Engineering, vol. 393, Springer Verlag, pp. 365-371, 11th International Conference on Future Information Technology, FutureTech 2016, Beijing, China, 20/04/16. https://doi.org/10.1007/978-981-10-1536-6_48

Modeling and simulation of PV modules based on ANFIS. / Bi, Ziqiang; Ma, Jieming; Hao, Wanjun et al.
Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE. ed. / Hai Jin; Young-Sik Jeong; Muhammad Khurram Khan; James J. Park. Springer Verlag, 2016. p. 365-371 (Lecture Notes in Electrical Engineering; Vol. 393).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Modeling and simulation of PV modules based on ANFIS

AU - Bi, Ziqiang

AU - Ma, Jieming

AU - Hao, Wanjun

AU - Pan, Xinyu

AU - Wang, Jian

AU - Ban, Jianmin

AU - Man, Ka Lok

N1 - Publisher Copyright: © Springer Science+Business Media Singapore 2016.

PY - 2016

Y1 - 2016

N2 - This work presents an optimized method to simulate the modeling of photovoltaic (PV) modules with measured data of PV array. The current-voltage (I-V) characteristics are estimated via adaptive neuro-fuzzy inference system (ANFIS). The proposed ANFIS method takes advantages of no need of internal parameters of PV model and can achieve a more accurate estimation of PV characteristics. By compared with Villalva’s model, radial basis function neural networks (RBFNN) and support vector machine (SVM) method, the results predicted by the proposed ANFIS approach show the best estimation performance in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2).

AB - This work presents an optimized method to simulate the modeling of photovoltaic (PV) modules with measured data of PV array. The current-voltage (I-V) characteristics are estimated via adaptive neuro-fuzzy inference system (ANFIS). The proposed ANFIS method takes advantages of no need of internal parameters of PV model and can achieve a more accurate estimation of PV characteristics. By compared with Villalva’s model, radial basis function neural networks (RBFNN) and support vector machine (SVM) method, the results predicted by the proposed ANFIS approach show the best estimation performance in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2).

KW - ANFIS

KW - Characteristic estimation

KW - Modeling

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SN - 9789811015359

T3 - Lecture Notes in Electrical Engineering

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BT - Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE

A2 - Jin, Hai

A2 - Jeong, Young-Sik

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T2 - 11th International Conference on Future Information Technology, FutureTech 2016

Y2 - 20 April 2016 through 22 April 2016

ER -

Modeling and simulation of PV modules based on ANFIS

Abstract

Publication series

Conference

Keywords

UN SDGs

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