PREDICTION OF SOLAR ENERGY PRODUCED UNDER DIFFERENT SYSTEM AND ENVIRONMENTAL CONDITIONS FOR JORDANIAN STATIONS USING ARTIFICIAL NEURAL NETWORK
Solar energy; as one of renewable energy sources, is of high importance mainly for countries with high temperature and long sunshine duration. However, environmental conditions and system parameters affect the output of the solar panels in different geographical locations in any country. Solar energy stations available in different locations in Jordan have been investigated, using artificial neural network (ANN). Analysis of several inputs (variables) identified were employed to indicate their relative significance to the output such as latitude, altitude, sunshine duration (SSD) and global solar radiation (GSR). ANN shows proficiency in the prediction of the original experimental data for all the solar stations. In the simulation, the energy gain increases with the increase in the GSR which is one important environmental condition and the perfect fit (R value 0.9961) indicates that the network output is close to targets. It can be concluded that the provided ANN model predicts power variable close to measured value. The uniqueness of this work is that it predicts the important output of the solar stations based on the logical arrangement of detailed parameters that are found in all operational units of the system.
EHAB HUSSEIN BANI-HANI, NEGIN MISAGHIAN AND JESSICA LOPEZ