Prediction of Monthly Average Daily Global Solar Radiation in Al Ain City –UAE Using Artificial Neural Networks

Measured air temperature, relative humidity, wind and sunshine duration measurements between 1995 and 2007 for Al Ain city in United Arab Emirates (UAE) were used for the estimation of monthly average daily global radiation on horizontal using Artificial Neural Network technique. Weather data between 1995 and 2006 were used for training the neural network, while the data of year 2007 was used for validation. The predications of Global Solar Radiation (GSR) were made using four combinations of data sets namely: 1) Sunshine, Temperature, Humidity and wind 2) Sunshine, Temperature and Humidity 3) Sunshine, Temperature and wind 4) Sunshine, wind and Humidity and 5) Temperature, Wind and Humidity. The ANN models with different input parameters have R = 0.87883 or higher, RMSE values vary between 0.276 to 0.39118 and small MBE ranging from -0.00013749 to 0.0000882. Key-Words: Monthly Average Daily Global Solar Radiation, Meteorology, Artificial Neural Network, modeling, Root Mean Square Error, Mean Bias Error