Solar Energy Prediction for Malaysia Using Artificial Neural Networks
This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2012/419504 |
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| author | Tamer Khatib Azah Mohamed K. Sopian M. Mahmoud |
| author_facet | Tamer Khatib Azah Mohamed K. Sopian M. Mahmoud |
| author_sort | Tamer Khatib |
| collection | DOAJ |
| description | This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods. |
| format | Article |
| id | doaj-art-678e0ab1993d4ed892685a52cad6cb25 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-678e0ab1993d4ed892685a52cad6cb252025-02-03T06:11:06ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2012-01-01201210.1155/2012/419504419504Solar Energy Prediction for Malaysia Using Artificial Neural NetworksTamer Khatib0Azah Mohamed1K. Sopian2M. Mahmoud3Department of Electrical, Electronic & System Engineering, Faculty of Engineering & Built Environment, National University of Malaysia, 43600 Bangi, Selangor, MalaysiaDepartment of Electrical, Electronic & System Engineering, Faculty of Engineering & Built Environment, National University of Malaysia, 43600 Bangi, Selangor, MalaysiaSolar Energy Research Institute, University Kebangsaan Malaysia, 43600 Bangi, Selangor, MalaysiaDepartment of Electrical Engineering, Engineering Faculty, An-Najah National University, Nablus 97300, PalestineThis paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.http://dx.doi.org/10.1155/2012/419504 |
| spellingShingle | Tamer Khatib Azah Mohamed K. Sopian M. Mahmoud Solar Energy Prediction for Malaysia Using Artificial Neural Networks International Journal of Photoenergy |
| title | Solar Energy Prediction for Malaysia Using Artificial Neural Networks |
| title_full | Solar Energy Prediction for Malaysia Using Artificial Neural Networks |
| title_fullStr | Solar Energy Prediction for Malaysia Using Artificial Neural Networks |
| title_full_unstemmed | Solar Energy Prediction for Malaysia Using Artificial Neural Networks |
| title_short | Solar Energy Prediction for Malaysia Using Artificial Neural Networks |
| title_sort | solar energy prediction for malaysia using artificial neural networks |
| url | http://dx.doi.org/10.1155/2012/419504 |
| work_keys_str_mv | AT tamerkhatib solarenergypredictionformalaysiausingartificialneuralnetworks AT azahmohamed solarenergypredictionformalaysiausingartificialneuralnetworks AT ksopian solarenergypredictionformalaysiausingartificialneuralnetworks AT mmahmoud solarenergypredictionformalaysiausingartificialneuralnetworks |