Real-Time AI-Based Power Demand Forecasting for Peak Shaving and Consumption Reduction Using Vehicle-to-Grid and Reused Energy Storage Systems: A Case Study at a Business Center on Jeju Island

Abstract

The increasing demand for electricity and the environmental challenges associated with traditional fossil fuel-based power generation have accelerated the global transition to renewable energy sources. While renewable energy offers significant advantages, including low carbon emissions and sustainability, its inherent variability and intermittency create challenges for grid stability and energy management. This study contributes to addressing these challenges by developing an AI-driven power consumption forecasting system. The core of the proposed system is a multi-cluster long short-term memory model (MC-LSTM), which combines k-means clustering with LSTM neural networks to enhance forecasting accuracy. The MC-LSTM model achieved an overall prediction accuracy of 97.93%, enabling dynamic, real-time demand-side energy management. Furthermore, to validate its effectiveness, the system integrates vehicle-to-grid technology and reused energy storage systems as external energy sources. A real-world demonstration was conducted in a commercial building on Jeju Island, where the AI-driven system successfully reduced total energy consumption by 21.3% through optimized peak shaving and load balancing. The proposed system provides a practical framework for enhancing grid stability, optimizing energy distribution, and reducing dependence on centralized power systems.