Deep Learning Forecasting of Renewable Energy Generation Patterns

Renewable energy sources, such as wind and solar, are unable to be integrated into present electrical infrastructures due to persistent issues with intermittency and unpredictability. Reliable load pattern forecasting for renewable energy sources and efficient energy storage use are essential for efficient power grid management. For renewable energy data, the intricacies that conventional forecasting techniques, such as autoregressive or statistical approaches, miss include nonlinearities, complicated dynamics, and strong temporal correlations. Forecasting the production of renewable energy sources accurately is therefore vital for optimizing energy storage use and maintaining system stability. This study proposes a CNN-LSTM architecture-integrated deep learning-based forecasting framework to enhance comprehension of the spatial feature representations and temporal correlations in energy and weather historical data. Using real-world wind and solar datasets, the hybrid CNN-LSTM model achieved a prediction accuracy that was 15-22% higher than that of baseline statistical and machine learning approaches such as Random Forest and ARIMA. Innovative dual-stage design combines optimum training approach for non-stationary renewable data with temporal-spatial feature learning to boost flexibility for grid-level deployment. The article also explores the possibilities of Transformers and federated learning frameworks for distributed renewable energy forecasting.