Predicting total solar irradiation values using artificial neural networks

J. Mubiru

Renewable Energy, 2008, vol. 33, issue 10, 2329-2332

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 0.018MJ/m2 and root mean square error of 0.131MJ/m2. Overall, the artificial neural networks model predicted with an accuracy of 0.1% of the mean absolute percentage error.

Keywords: Artificial neural networks; Solar radiation; Prediction; Model (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:33:y:2008:i:10:p:2329-2332

DOI: 10.1016/j.renene.2008.01.009

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