Identification of Relevant Input Variables for Prediction of Output PV Power Using Artificial Neural Network Models

At present, generating energy from renewable sources is an important topic and is attracting significant attention because of its many benefits. Recent technological developments have made generating renewable energy from various sources such as the solar, sun, wind, geothermal energy, With the continuous increase of grid-connected photovoltaic (PV), high-precision PV power prediction is increasingly important. Extant deterministic forecasting methods do not facilitate fully effective dispatching decisions or power grid risk analysis. To find the most influencing variables for PV power prediction, this paper proposes a model for predicting the output PV power, different combinations of weather variables were used to develop this model. The determination coefficient of the proposed model is 0.98501 with an RMSE value of 30.663. The proposed model was tested on new data, the results showed that the model works with a good preferment and that the prediction quality depends on the time of year with a determination coefficient of 0.9972, 0.9856, 0.9487 and 0.9942 for summer, autumn, winter and spring respectively.