Research on Data-Driven Dynamic Reconfiguration and Energy-Saving Optimization Methods for Building Energy-Consuming Microgrids
This study proposes a data-driven dynamic reconfiguration and energy-saving optimization framework for building energy-consuming microgrids. By integrating multi-source sensing data, load forecasting, and dynamic topology optimization, the framework establishes a coupling mechanism between building energy consumption and microgrid operation. A hierarchical architecture consisting of data acquisition, intelligent prediction, optimization decisionmaking, and execution control is developed to support adaptive energy management under varying load conditions. Load forecasting is performed using a long short-term memory network, while an enhanced particle swarm optimization algorithm is employed to determine optimal microgrid reconfiguration strategies under operational constraints. Case studies based on commercial building scenarios demonstrate that the proposed framework effectively improves load regulation capability, enhances renewable energy utilization, and supports stable microgrid operation. The framework can be readily integrated with wireless sensing infrastructures, antenna-enabled monitoring platforms, and edge–cloud collaborative systems, providing an engineering-oriented solution for real-time energy management and intelligent microgrid optimization in modern building environments.