Effect of Solar and Weather Parameters on LSTM-based Model Predictions of Solar Power Production

This paper discusses the process of training a neural network intended for predicting power generation in a solar power plant based on meteorological data and the historical data of power measurement readings. Initially, the neural network was trained on a wide array of different solar and weather quantities as inputs (26 in total), but since the prediction errors of this model were considerably large, the model was retrained, but on a much smaller subset of critical input variables, which resulted in an increase of accuracy where MAE dropped significantly.