A Novel Method Forecasting for Big Data Analytics in Microgrid Energy Management

With the rapid proliferation of renewable energy sources (RES) in modern power grids, the application of operational optimization strategies has become pivotal in maintaining system efficiency and stability. In particular, the hybrid deep learning model long short-term memory (LSTM) neural network combined with inductive conformal prediction (ICP) significantly enhances the accuracy of renewable energy forecasting and management. This paper presents the integration of big data analytics (BDA) with the LSTM+ICP framework for optimizing smart grid operations, particularly under real-time conditions. A suite of machine learning and deep learning models, especially the hybrid LSTM+ICP, is deployed to address critical challenges such as renewable output forecasting, load balancing, and fault detection. Owing to its capability to capture temporal dependencies and generate reliable prediction intervals, the LSTM+ICP model achieved a mean absolute percentage error (MAPE) of 2.91%, thus improving the reliability of renewable energy scheduling and enabling more efficient resource allocation. The implementation of real-time BDA in conjunction with LSTM+ICP reduced load variance by 44.3%, peak demand by 24.2%, and frequency deviations by 52.9%, thereby strengthening grid reliability and operational stability. In predictive maintenance, the LSTM+ICP model achieved a detection accuracy of 97.2% with an average lead time of 6.2 hours, enabling proactive interventions and minimizing fault risks. For system optimization, the application of reinforcement learning augmented by BDA led to a 33.9% reduction in power losses, a 22.4% increase in voltage stability, and a 29.1% decrease in reactive power, thereby enhancing operational efficiency. From both economic and environmental perspectives, the BDA-driven approach resulted in monthly cost savings of C36,150 and a 30.2% reduction in CO2 emissions, demonstrating the efficacy and sustainability of the proposed methodology.