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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2014/469701 |
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| _version_ | 1832559887768354816 |
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| author | Aminmohammad Saberian H. Hizam M. A. M. Radzi M. Z. A. Ab Kadir Maryam Mirzaei |
| author_facet | Aminmohammad Saberian H. Hizam M. A. M. Radzi M. Z. A. Ab Kadir Maryam Mirzaei |
| author_sort | Aminmohammad Saberian |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-33eed825147d4f579c72661cc5d50647 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-33eed825147d4f579c72661cc5d506472025-02-03T01:29:00ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2014-01-01201410.1155/2014/469701469701Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural NetworksAminmohammad Saberian0H. Hizam1M. A. M. Radzi2M. Z. A. Ab Kadir3Maryam Mirzaei4Department of Electrical and Electronic Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Malaysia Department of Electrical and Electronic Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Malaysia Department of Electrical and Electronic Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Malaysia Department of Electrical and Electronic Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Malaysia Department of Electrical and Electronic Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Malaysia 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.http://dx.doi.org/10.1155/2014/469701 |
| spellingShingle | Aminmohammad Saberian H. Hizam M. A. M. Radzi M. Z. A. Ab Kadir Maryam Mirzaei Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks International Journal of Photoenergy |
| 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://dx.doi.org/10.1155/2014/469701 |
| work_keys_str_mv | AT aminmohammadsaberian modellingandpredictionofphotovoltaicpoweroutputusingartificialneuralnetworks AT hhizam modellingandpredictionofphotovoltaicpoweroutputusingartificialneuralnetworks AT mamradzi modellingandpredictionofphotovoltaicpoweroutputusingartificialneuralnetworks AT mzaabkadir modellingandpredictionofphotovoltaicpoweroutputusingartificialneuralnetworks AT maryammirzaei modellingandpredictionofphotovoltaicpoweroutputusingartificialneuralnetworks |