Predicting total solar irradiation values using artificial neural networks |
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Authors: | J Mubiru |
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Affiliation: | aDepartment of Physics, Makerere University, P.O. Box 7062, Kampala, Uganda |
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Abstract: | This study explores the possibility of developing an artificial neural networks model that could be used to predict monthly average daily total solar irradiation on a horizontal surface for locations in Uganda based on geographical and meteorological data: latitude, longitude, altitude, sunshine duration, relative humidity and maximum temperature. Results have shown good agreement between the predicted and measured values of total solar irradiation. A correlation coefficient of 0.997 was obtained with mean bias error of 0.018 MJ/m2 and root mean square error of 0.131 MJ/m2. Overall, the artificial neural networks model predicted with an accuracy of 0.1% of the mean absolute percentage error. |
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Keywords: | Artificial neural networks Solar radiation Prediction Model |
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