Estimation Of Monthly Average Daily Global Solar Irradiation Using Artificial Neural Networks - Mubiru, J.; Banda, E.J.K.B. (Solar Energy, 2008)
This study explores the possibility of developing a prediction model using artificial neural networks (ANN), which could be used to estimate monthly average daily global solar irradiation on a horizontal surface for locations in Uganda based on weather station data: sunshine duration, maximum temperature, cloud cover and location parameters: latitude, longitude, altitude. Results have shown good agreement between the estimated and measured values of global solar irradiation. A correlation coefficient of 0.974 was obtained with mean bias error of 0.059 MJ/m2 and root mean square error of 0.385 MJ/m2. The comparison between the ANN and empirical method emphasized the superiority of the proposed ANN prediction model.
Keywords: Artificial neural networks; Global solar irradiation; Sunshine hours; Cloud cover; Maximum temperature; Model