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Silicon PV module fitting equations based on experimental measurements

Solar photovoltaic (PV) characteristic curves (P-V and I-V) offer the information required to configure the PV system to operate as near to its optimal performance as possible. Measurement-based modeling can provide an accurate description for this purpose. This work analyzes the PV module performan...

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Main Authors: Sabry, A.H., Hasan, W.Z.W., Sabri, Y.H., Ab-Kadir, M.Z.A.
Format: Article
Language:English
Published: 2020
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spelling my.uniten.dspace-131592020-07-06T02:45:09Z Silicon PV module fitting equations based on experimental measurements Sabry, A.H. Hasan, W.Z.W. Sabri, Y.H. Ab-Kadir, M.Z.A. Solar photovoltaic (PV) characteristic curves (P-V and I-V) offer the information required to configure the PV system to operate as near to its optimal performance as possible. Measurement-based modeling can provide an accurate description for this purpose. This work analyzes the PV module performance and develops a mathematical formula under particular weather conditions to accurately express these curves based on a custom neural network (CNN). The study initially presents several standard mathematical model equations, such as polynomial, exponential, and Gaussian models to fit the PV module measurements. The model selection is subjected to the minimum value of an evaluation parameter. To simplify the solution of the symbolic equations for the CNN network, two neurons in the hidden layer with nonlinear activation function and linear for the output layer were selected. The results show the effectiveness of the proposed CNN model equations over other standard fitting models according to the root mean squared error (RMSE) evaluation. This method promises further improved results with multi-input parameter modeling. © 2018 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd. 2020-02-03T03:30:48Z 2020-02-03T03:30:48Z 2019 Article 10.1002/ese3.264 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description Solar photovoltaic (PV) characteristic curves (P-V and I-V) offer the information required to configure the PV system to operate as near to its optimal performance as possible. Measurement-based modeling can provide an accurate description for this purpose. This work analyzes the PV module performance and develops a mathematical formula under particular weather conditions to accurately express these curves based on a custom neural network (CNN). The study initially presents several standard mathematical model equations, such as polynomial, exponential, and Gaussian models to fit the PV module measurements. The model selection is subjected to the minimum value of an evaluation parameter. To simplify the solution of the symbolic equations for the CNN network, two neurons in the hidden layer with nonlinear activation function and linear for the output layer were selected. The results show the effectiveness of the proposed CNN model equations over other standard fitting models according to the root mean squared error (RMSE) evaluation. This method promises further improved results with multi-input parameter modeling. © 2018 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
format Article
author Sabry, A.H.
Hasan, W.Z.W.
Sabri, Y.H.
Ab-Kadir, M.Z.A.
spellingShingle Sabry, A.H.
Hasan, W.Z.W.
Sabri, Y.H.
Ab-Kadir, M.Z.A.
Silicon PV module fitting equations based on experimental measurements
author_facet Sabry, A.H.
Hasan, W.Z.W.
Sabri, Y.H.
Ab-Kadir, M.Z.A.
author_sort Sabry, A.H.
title Silicon PV module fitting equations based on experimental measurements
title_short Silicon PV module fitting equations based on experimental measurements
title_full Silicon PV module fitting equations based on experimental measurements
title_fullStr Silicon PV module fitting equations based on experimental measurements
title_full_unstemmed Silicon PV module fitting equations based on experimental measurements
title_sort silicon pv module fitting equations based on experimental measurements
publishDate 2020
_version_ 1672614211328409600
score 13.222552

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