Multi-Source Renewable Energy Forecasting Using CNN-LSTM Hybrid Model

Accurate forecasting of renewable energy output is vital for enhancing the efficiency and stability of integrated power systems. This study introduces a novel deep learning approach that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks to predict energy production from solar, wind and hydropower sources. The hybrid CNN-LSTM model utilizes historical meteorological data, including solar irradiance, cloud cover, temperature, wind speed, and precipitation, to forecast future energy generation with high precision. The CNN component extracts spatial features from the weather data, while the LSTM component captures temporal dependencies and long-term trends. For solar energy forecasting, the model estimates solar radiation based on factors like temperature and humidity, while wind energy predictions rely on wind speed variations, and hydropower output is forecasted using rainfall and watershed information. This comprehensive approach enables accurate and reliable predictions of renewable energy production. The model's performance is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>), etc. Results demonstrate that the hybrid model significantly improves the accuracy and reliability of renewable energy forecasts. This advanced tool provides valuable insights for energy managers, utility companies, and policymakers, aiding in optimizing resource allocation and enhancing the sustainability and resilience of energy grids.