This paper attempts to develop a prediction model using the artificial neural network (ANN) [6, 7] for estimating the monthly average daily solar irradiation in the city of Jeddah, Kingdom of Saudi Arabia but, may be extended to other cities of the Kingdom. An in-depth ANN forecast method for solar irradiation is presented along with the statistical approach and techniques for predicting the Global Horizontal Irradiation (GHI).      By using case examples, it is possible to build models capable of predicting and generating rules that can be translated into natural query language and provide a measure of the confidence of the classification on the basis of its attributes. The data used in this study uses attributes such as variable weather and solar irradiation data of 10 cities provided by KACARE [8] as part of the Renewable Resource Monitoring and Mapping (RRMM) Program. The simulation is performed at Jeddah in an attempt to compare with the experimental measurements by KACARE and measurements done at Effat Solar PV system installed at the roof of the Deanship for Graduate Studies and Research (DGSR). An example of solar radiation and air temperature distributions computed using RETScreen [9], an energy management software created by the Government of Canada, to help predicting the viability and feasibility of energy project including renewable energy sources, is given in Figure 2. ANN method may use up to 12 attributes with MATLAB and/or WEKA [10]. The set of attributes being used for training are namely the time of the day, the year, the latitude and longitude, the air temperature, the wind speed and its direction, the azimuth angle, the diffuse horizontal irradiance, the direct horizontal irradiance, the global horizontal irradiance, the humidity, the pressure, and the zenith angle.

The significance of this study relies on its capability of predicting the solar irradiation to quantify and improve the PV system design, to ensure a secure and reliable electrical output, and to help electrical grid operators to manage the entire grid system. Another key element of this study is the outreach and dissemination of renewable energy technologies on Effat University campus, in addition to providing students with opportunities to perform experiments on the installed PV systems and to help Effat University becoming a leader in best practice campus sustainability.