## 1 Introduction
In particular, approaches using linear programming have been employed to achieve a stable and reliable power supply in the presence of limited available energy sources [11]. Other studies have employed stochastic optimization [12], which utilizes the probability density function of RESs to schedule system operations, along with robust optimization methods [13] that ensure the feasibility of optimization results under worst-case scenarios. To further enhance the effectiveness of these diverse strategies, extensive research has been conducted on direct demand forecasting techniques. By predicting future peak loads and considering load uncertainty, more efficient energy management becomes possible. A wide range of statistical models have been explored for power consumption forecasting, including time-series analysis and regression analysis [14]. However, the nonlinearity and complex interactions within the data cannot be fully captured by these traditional approaches [15]. Over the past decade, machine learning has emerged as a powerful tool for energy consumption forecasting, distinguishing itself in various fields. Machine learning techniques--such as artificial neural networks [16] and support vector machines [17]--have emerged as powerful tools to identify complex correlations and trends in power consumption data, thereby generating reliable predictions. More recently, various deep learning-based forecasting methods, such as recurrent neural networks [18] and convolutional neural networks [19], have also been proposed. In line with these developments, this study proposes an AI-driven power consumption forecasting system. The core of this system is a multi-cluster long short-term memory (MC-LSTM) model, which combines k-means clustering with LSTM neural networks to achieve highly accurate power demand predictions. By clustering the entire power consumption data--which exhibits a wide range of variability--into groups with similar characteristics, each model can be fine-tuned to its respective cluster's features. To evaluate the effectiveness of this AI-based power demand forecasting system, the study integrates vehicle-to-grid (V2G) technology and reused energy storage systems (R-ESSs) as validation tools. V2G enables bidirectional energy exchange between EVs and the grid, allowing EVs to function as short-term distributed energy storage units, while the R-ESS, repurposed from electric bus batteries, provides long-term storage to mitigate fluctuations in renewable energy generation. By combining AI-driven forecasting with these energy storage solutions, the system dynamically allocates resources, reducing peak loads and stabilizing power demand. The proposed system was implemented in a commercial building within Jeju Technovalley to assess its real-world impact on peak power reduction and load optimization. Jeju Island has emerged as an ideal demonstration site due to its ambitious renewable energy targets, unique grid challenges arising from geographic isolation, and progressive smart grid initiatives. The island's dynamic energy landscape, driven by factors such as increased tourism and EV adoption, further underscores its suitability as a testing ground for innovative energy management solutions. Unlike many previous studies that evaluated forecasting policy through simulation on a validation set, our study extends the evaluation to a real-world case study, demonstrating the model's practical applicability. This case study provides valuable insights into how an AI-driven demand forecasting system can contribute to energy management for island regions, microgrids, and energy-independent communities, demonstrating a scalable solution for future smart grids and autonomous energy networks.