Spatio Temporal Analysis of Solar Energy Dataset across Andhra Pradesh, Rajasthan, Gujarat, Tamil Nadu, Telangana, Karnataka using SARIMA, MLP and LSTM

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.

This Python script implements three hybrid deep learning models (CNN-LSTM-GRU, LSTM-CNN-RNN, LSTM-GRU-RNN) for solar energy radiation prediction. It preprocesses time-series data, trains the models, and evaluates their performance using metrics like MSE and RMSE, optimizing for accurate solar energy forecasting.

LSTM-based deep learning models for forecasting solar and wind energy production using time series data.

Deep learning–based time-series forecasting of solar power generation using multivariate weather and temporal features. Implements LSTM, CNN, and CNN-LSTM models to predict future solar output, with feature engineering and evaluation using standard forecasting metrics for renewable energy analysis.