Modelling and prediction of photovoltaic power output using artificial neural networks
This paper presents a solar power modelling method using artificial neural networks (ANNs). Two neural network structures, namely, general regression neural network (GRNN) feedforward back propagation (FFBP), have been used to model a photovoltaic panel output power and approximate the generated pow...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Publishing Corporation
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/34553/ http://psasir.upm.edu.my/id/eprint/34553/1/Modelling%20and%20Prediction%20of%20Photovoltaic%20Power%20Output%20Using.pdf |
| _version_ | 1848847805286711296 |
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| author | Saberian, Aminmohammad Hizam, Hashim Mohd Radzi, Mohd Amran Ab Kadir, Mohd Zainal Abidin Mirzaei, Maryam |
| author_facet | Saberian, Aminmohammad Hizam, Hashim Mohd Radzi, Mohd Amran Ab Kadir, Mohd Zainal Abidin Mirzaei, Maryam |
| author_sort | Saberian, Aminmohammad |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents a solar power modelling method using artificial neural networks (ANNs). Two neural network structures, namely, general regression neural network (GRNN) feedforward back propagation (FFBP), have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN. |
| first_indexed | 2025-11-15T09:24:26Z |
| format | Article |
| id | upm-34553 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:24:26Z |
| publishDate | 2014 |
| publisher | Hindawi Publishing Corporation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-345532019-11-21T06:45:04Z http://psasir.upm.edu.my/id/eprint/34553/ Modelling and prediction of photovoltaic power output using artificial neural networks Saberian, Aminmohammad Hizam, Hashim Mohd Radzi, Mohd Amran Ab Kadir, Mohd Zainal Abidin Mirzaei, Maryam This paper presents a solar power modelling method using artificial neural networks (ANNs). Two neural network structures, namely, general regression neural network (GRNN) feedforward back propagation (FFBP), have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN. Hindawi Publishing Corporation 2014 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/34553/1/Modelling%20and%20Prediction%20of%20Photovoltaic%20Power%20Output%20Using.pdf Saberian, Aminmohammad and Hizam, Hashim and Mohd Radzi, Mohd Amran and Ab Kadir, Mohd Zainal Abidin and Mirzaei, Maryam (2014) Modelling and prediction of photovoltaic power output using artificial neural networks. International Journal of Photoenergy, 2014. art. no. 469701. pp. 1-10. ISSN 1110-662X; ESSN: 1687-529X http://www.hindawi.com/journals/ijp/2014/469701/abs/ 10.1155/2014/469701 |
| spellingShingle | Saberian, Aminmohammad Hizam, Hashim Mohd Radzi, Mohd Amran Ab Kadir, Mohd Zainal Abidin Mirzaei, Maryam Modelling and prediction of photovoltaic power output using artificial neural networks |
| title | Modelling and prediction of photovoltaic power output using artificial neural networks |
| title_full | Modelling and prediction of photovoltaic power output using artificial neural networks |
| title_fullStr | Modelling and prediction of photovoltaic power output using artificial neural networks |
| title_full_unstemmed | Modelling and prediction of photovoltaic power output using artificial neural networks |
| title_short | Modelling and prediction of photovoltaic power output using artificial neural networks |
| title_sort | modelling and prediction of photovoltaic power output using artificial neural networks |
| url | http://psasir.upm.edu.my/id/eprint/34553/ http://psasir.upm.edu.my/id/eprint/34553/ http://psasir.upm.edu.my/id/eprint/34553/ http://psasir.upm.edu.my/id/eprint/34553/1/Modelling%20and%20Prediction%20of%20Photovoltaic%20Power%20Output%20Using.pdf |