Solar Power forecasting for a Solar Power System is a very active research field, as reliable information about the future power generation allow for a safe operation of the power grid and helps to minimize the operational costs. Deep Learning algorithms have shown to be very powerful in forecasting tasks, such as economic time series or speech recognition. Up to now, Deep Learning algorithms have only been applied sparsely for forecasting renewable energy power plants. By using different Deep Learning Algorithms, such as RNN,LSTM with different Feature extraction method like PCA,1D CNN , Auto encoders , My motive is to show the forecast strength of these algorithms compared to a standard MLP and traditional machine learning model in forecasting the energy output of 21 solar power plants.
