Most Influential Variables for Solar Radiation Forecasting Using Artificial Neural Networks

Decaying fossil fuel resources, international relation complexities, and the risks associated with nuclear power have led to an increased demand for alternative energy sources. Renewable energy sources offer adequate solutions to these challenges. Forecasting of solar energy has also increased over the past decade due to its use in photovoltaic (PV) system design, load balance in hybrid systems, and projected potential future PV system feasibility. Artificial neural networks (ANN) have been used successfully for solar energy forecasting. In this work, several meteorological variables from the Solar Village in Riyadh, Saudi Arabia are used as a case study to determine the most effective variables for Global Solar Radiation (GSR) prediction. Those variables are then used as inputs for a proposed GSR prediction model. This model will be applicable in different locations and conditions, and has a simple structure and offers better results in terms of error between actual and predicted solar radiation values.