Adaptive Solar Energy Storage with Deep Learning for Improved Grid Resilience

Implementing renewable energy sources, especially solar power, into the electrical grid has distinct difficulties and potential for improving system resilience. This research investigates an adaptive solar energy storage system using deep learning methodologies, particularly Long Short-Term Memory (LSTM) networks, to enhance energy management and grid stability. The proposed system utilizes past solar generating data and usage patterns to forecast future energy needs and generation capacities. LSTM allows the model to efficiently reflect temporal interdependence and variation in solar energy output, facilitating more precise forecasting. The adaptive storage system modifies its charging and discharging processes in real time, optimizing efficiency and reducing energy waste. LSTM-based forecasting improves the storage system’s response to variations in supply and demand, enhancing load balancing and grid resilience. This method facilitates a change in renewable energy sources while enhancing the resilience of grid infrastructure to sustain delays. Findings indicate that integrating flexible energy storage using advanced deep-learning methodologies may significantly improve the efficacy and dependability of solar energy systems. This paper challenges solar energy intermittency with LSTM-based adaptive storage, enhancing forecasting precision, grid resilience, and energy efficiency enhancing stable energy management and optimized battery use.