Hybrid LSTM-Fuzzy Logic Model for Accurate and Robust Energy Forecasting in Renewable-Powered Smart Grids
The integration of renewable energy sources into smart grid systems presents significant challenges for accurate energy forecasting due to the inherent intermittency and stochastic nature of solar and wind power generation. This research proposes a novel hybrid forecasting model that synergistically combines Long Short-Term Memory (LSTM) neural networks with Fuzzy Logic inference systems to achieve superior prediction accuracy while maintaining robustness against data uncertainties and external disturbances. The LSTM component 006Futperforms at capturing complex temporal dependencies and non-linear patterns in historical energy data, while the fuzzy logic module provides interpretable decision-making capabilities and handles linguistic uncertainties through expert knowledge integration. Experimental validation using real-world datasets from a renewable-powered microgrid demonstrates that the proposed hybrid model achieves a Mean Absolute Percentage Error (MAPE) of 3.2% for short-term load forecasting, representing a 53% improvement over standalone LSTM models and a 61% improvement over conventional fuzzy inference systems. Furthermore, the model exhibits remarkable robustness to noisy data conditions, maintaining prediction accuracy within acceptable bounds even when subjected to up to 20% data corruption. The results establish the hybrid LSTM-Fuzzy approach as a highly effective solution for energy forecasting in modern smart grid applications, with significant implications for grid stability, renewable energy integration, and demand-side management strategies