Predicting total solar irradiation values using artificial neural networks

详细信息    查看全文

• 作者：J. Mubiru
• 关键词：Artificial neural networks ; Solar radiation ; Prediction ; Model
• 刊名：Renewable Energy
• 出版年：2008
• 出版时间：October 2008
• 年：2008
• 卷：33
• 期：10
• 页码：2329-2332
• 全文大小：164 K

文摘

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.