The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS)
The prediction of solar radiation is very important tool in climatology, hydrology and energy applications, as it permits estimating solar data for locations where measurements are not available. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is presented to predict the monthly glob...
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| Format: | Article |
| Language: | Arabic |
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Libyan Center for Solar Energy REsearch and Studies
2016-12-01
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| Series: | Solar Energy and Sustainable Development |
| Subjects: | |
| Online Access: | http://www.jsesd.csers.ly/index.php/en/journal-papers/26-vol-05-2/95-vol-005-2-5 |
| _version_ | 1797710761911386112 |
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| author | Muna A. Alzukrah Yosof M. Khalifa |
| author_facet | Muna A. Alzukrah Yosof M. Khalifa |
| author_sort | Muna A. Alzukrah |
| collection | DOAJ |
| description | The prediction of solar radiation is very important tool in climatology, hydrology and energy applications, as it permits estimating solar data for locations where measurements are not available. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is presented to predict the monthly global solar radiation on a horizontal surface in Libya. The real meteorological solar radiation data from 5 stations for the period of 1982 - 2009 with different latitudes and longitudes were used in the current study. The data set is divided into two subsets; the fist is used for training and the latter is used for testing the model. (ANFIS) combines fuzzy logic and neural network techniques that are used in order to gain more efficiency. The statistical performance parameters such as root mean square error (RMSE), mean absolute percentage error (MAPE) and the coefficient of efficiency (E) were calculated to check the adequacy of the model. On the basis of coefficient of efficiency, as well as the scatter diagrams and the error modes, the predicted results indicate that the neuro-fuzzy model gives reasonable results: accuracy of about 92% - 96% and the RMSE ranges between 0.22 - 0.35 kW.hr/m2/day |
| first_indexed | 2024-03-12T06:55:56Z |
| format | Article |
| id | doaj.art-4a1b3f824d6e4afb9a985a36c83c9792 |
| institution | Directory Open Access Journal |
| issn | 2411-9636 2414-6013 |
| language | Arabic |
| last_indexed | 2024-03-12T06:55:56Z |
| publishDate | 2016-12-01 |
| publisher | Libyan Center for Solar Energy REsearch and Studies |
| record_format | Article |
| series | Solar Energy and Sustainable Development |
| spelling | doaj.art-4a1b3f824d6e4afb9a985a36c83c97922023-09-03T00:01:22ZaraLibyan Center for Solar Energy REsearch and StudiesSolar Energy and Sustainable Development2411-96362414-60132016-12-01524452The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS)Muna A. Alzukrah0Yosof M. Khalifa1Department of Civil Engineering, Higher Institute for General Vocations, Agdabia-LibyaCentre for Solar Energy Research and Studies, Tajura, Tripoli-LibyaThe prediction of solar radiation is very important tool in climatology, hydrology and energy applications, as it permits estimating solar data for locations where measurements are not available. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is presented to predict the monthly global solar radiation on a horizontal surface in Libya. The real meteorological solar radiation data from 5 stations for the period of 1982 - 2009 with different latitudes and longitudes were used in the current study. The data set is divided into two subsets; the fist is used for training and the latter is used for testing the model. (ANFIS) combines fuzzy logic and neural network techniques that are used in order to gain more efficiency. The statistical performance parameters such as root mean square error (RMSE), mean absolute percentage error (MAPE) and the coefficient of efficiency (E) were calculated to check the adequacy of the model. On the basis of coefficient of efficiency, as well as the scatter diagrams and the error modes, the predicted results indicate that the neuro-fuzzy model gives reasonable results: accuracy of about 92% - 96% and the RMSE ranges between 0.22 - 0.35 kW.hr/m2/dayhttp://www.jsesd.csers.ly/index.php/en/journal-papers/26-vol-05-2/95-vol-005-2-5Adaptive Neuro-Fuzzy SystemFuzzy logicNeural NetworkMonthly Global Solar RadiationRootMean Square Error; |
| spellingShingle | Muna A. Alzukrah Yosof M. Khalifa The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Solar Energy and Sustainable Development Adaptive Neuro-Fuzzy System Fuzzy logic Neural Network Monthly Global Solar Radiation Root Mean Square Error; |
| title | The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS) |
| title_full | The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS) |
| title_fullStr | The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS) |
| title_full_unstemmed | The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS) |
| title_short | The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS) |
| title_sort | prediction of solar radiation for five meteorological stations in libya using adaptive neuro fuzzy inference system anfis |
| topic | Adaptive Neuro-Fuzzy System Fuzzy logic Neural Network Monthly Global Solar Radiation Root Mean Square Error; |
| url | http://www.jsesd.csers.ly/index.php/en/journal-papers/26-vol-05-2/95-vol-005-2-5 |
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