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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley Open Access
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/82122/ http://psasir.upm.edu.my/id/eprint/82122/1/Silicon%20PV%20module%20fitting%20equations%20based%20on%20experimental%20measurements%20.pdf |
| _version_ | 1848859241204416512 |
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| author | Sabry, Ahmad H. Wan Hasan, Wan Zuha Sabri, Yasameen H. Ab Kadir, Mohd Zainal Abidin |
| author_facet | Sabry, Ahmad H. Wan Hasan, Wan Zuha Sabri, Yasameen H. Ab Kadir, Mohd Zainal Abidin |
| author_sort | Sabry, Ahmad H. |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T12:26:12Z |
| format | Article |
| id | upm-82122 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:26:12Z |
| publishDate | 2019 |
| publisher | Wiley Open Access |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-821222021-08-11T06:34:00Z http://psasir.upm.edu.my/id/eprint/82122/ Silicon PV module fitting equations based on experimental measurements Sabry, Ahmad H. Wan Hasan, Wan Zuha Sabri, Yasameen H. Ab Kadir, Mohd Zainal Abidin 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. Wiley Open Access 2019-02 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/82122/1/Silicon%20PV%20module%20fitting%20equations%20based%20on%20experimental%20measurements%20.pdf Sabry, Ahmad H. and Wan Hasan, Wan Zuha and Sabri, Yasameen H. and Ab Kadir, Mohd Zainal Abidin (2019) Silicon PV module fitting equations based on experimental measurements. Energy Science and Engineering, 7 (1). pp. 132-145. ISSN 2050-0505 https://onlinelibrary.wiley.com/doi/10.1002/ese3.264 10.1002/ese3.264 |
| spellingShingle | Sabry, Ahmad H. Wan Hasan, Wan Zuha Sabri, Yasameen H. Ab Kadir, Mohd Zainal Abidin Silicon PV module fitting equations based on experimental measurements |
| title | 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_short | Silicon PV module fitting equations based on experimental measurements |
| title_sort | silicon pv module fitting equations based on experimental measurements |
| url | http://psasir.upm.edu.my/id/eprint/82122/ http://psasir.upm.edu.my/id/eprint/82122/ http://psasir.upm.edu.my/id/eprint/82122/ http://psasir.upm.edu.my/id/eprint/82122/1/Silicon%20PV%20module%20fitting%20equations%20based%20on%20experimental%20measurements%20.pdf |