Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model
The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizont...
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2016
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my.utm.691282017-11-23T01:43:39Z http://eprints.utm.my/id/eprint/69128/ Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model Gani, Abdullah Mohammadi, Kasra Shamshirband, Shahaboddin Khorasanizadeh, Hossein Danesh, Amir Seyed Piri, Jamshid Ismail, Zuraini Zamani, Mazdak TJ Mechanical engineering and machinery The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neuro-fuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44–0.61 kWh/m2, 0.50–0.71 kWh/m2, and 0.78–0.91, respectively. Springer-Verlag Wien 2016 Article PeerReviewed Gani, Abdullah and Mohammadi, Kasra and Shamshirband, Shahaboddin and Khorasanizadeh, Hossein and Danesh, Amir Seyed and Piri, Jamshid and Ismail, Zuraini and Zamani, Mazdak (2016) Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model. Theoretical and Applied Climatology, 125 (3-4). pp. 679-689. ISSN 0177-798X http://dx.doi.org/10.1007/s00704-015-1533-8 DOI:10.1007/s00704-015-1533-8 |
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TJ Mechanical engineering and machinery Gani, Abdullah Mohammadi, Kasra Shamshirband, Shahaboddin Khorasanizadeh, Hossein Danesh, Amir Seyed Piri, Jamshid Ismail, Zuraini Zamani, Mazdak Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model |
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The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neuro-fuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44–0.61 kWh/m2, 0.50–0.71 kWh/m2, and 0.78–0.91, respectively. |
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Article |
author |
Gani, Abdullah Mohammadi, Kasra Shamshirband, Shahaboddin Khorasanizadeh, Hossein Danesh, Amir Seyed Piri, Jamshid Ismail, Zuraini Zamani, Mazdak |
author_facet |
Gani, Abdullah Mohammadi, Kasra Shamshirband, Shahaboddin Khorasanizadeh, Hossein Danesh, Amir Seyed Piri, Jamshid Ismail, Zuraini Zamani, Mazdak |
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Gani, Abdullah |
title |
Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model |
title_short |
Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model |
title_full |
Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model |
title_fullStr |
Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model |
title_full_unstemmed |
Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model |
title_sort |
day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model |
publisher |
Springer-Verlag Wien |
publishDate |
2016 |
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http://eprints.utm.my/id/eprint/69128/ http://dx.doi.org/10.1007/s00704-015-1533-8 |
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