Prediction Method for Flexible Adjustment Capacity of Microgrid Groups Based on Improved LSTM Algorithm

The flexible regulation ability of microgrid clusters is related to the operational stability of microgrid clusters and even the overall power grid. we introduce an advanced prediction method for the flexible adjustment capacity of microgrid groups utilizing an Improved Long Short-Term Memory (LSTM) algorithm. Recognizing the complexity and dynamic nature of microgrid operations, the study aims to overcome the limitations of traditional forecasting models by enhancing the predictive accuracy and computational efficiency of LSTM networks. The proposed method integrates an optimization technique, the artificial fish swarm algorithm (AFSA), to fine-tune the LSTM parameters, leveraging the collective behavior of fish to navigate towards optimal solutions. The research systematically evaluates the improved LSTM model against standard forecasting methods across various performance metrics, demonstrating a significant increase in both accuracy and efficiency. Experimental results indicate the superior capability of the improved LSTM algorithm