Nonlinear Autoregressive Recurrent Neural Network Model For Solar Radiation Prediction

Solar Radiation (SR) is one of the most important parameters in the design of photovoltaic systems (PVs). An accurate evaluation of the SR of a given location is essential for the efficient design and utilization of PVs. In this paper, a nonlinear autoregressive recurrent neural networks with exogenous input (NARX) was used to predict the SR in Mutah city. Hourly, weather data of three variables (temperature, wind speed and humidity) were collected for the 2015 year. These data were used to construct seven NARX Artificial Neural Networks (ANN) with SR as the objective function in terms of the input variables. These seven models were investigated and analyzed to attain the most accurate and optimum model in prediction of the SR. This was performed by changing number of neurons and number of delays and then computing the mean squared error (MSE) and regression (R) values for each scenario. Model seven with inputs of temperature, wind speed and humidity was the most appropriate model to be used for predictions and investigations. In addition, the obtained results showed that the developed models were proficient to forecast the solar radiation and its capability to produce a precise estimates and predictions.