## 5 Conclusions  
This study developed and validated an AI-driven energy management system designed for power consumption forecasting and peak reduction in commercial buildings. The proposed MC-LSTM model, which integrates k-means clustering with LSTM networks, effectively classified power consumption data based on seasonal variations and work schedules. This clustering-based approach enabled tailored predictions for each group, significantly improving forecasting accuracy. The model achieved an average MAAPE-based accuracy of 97.93%, with individual cluster accuracies reaching up to 98.27%, particularly in structured environments such as commercial buildings where consumption patterns are stable and repeatable. These results underscore the effectiveness of our clustering-based approach in enabling robust AI-driven predictive modeling. Moreover, the ablation study confirmed that integrating multi-cluster techniques significantly enhances forecasting performance compared to the single neural network, demonstrating that our approach is versatile and adaptable to other neural network architectures beyond LSTM. The proposed model was applied in a real-world case study at a commercial building on Jeju Island. The system's real-time peak shaving strategy utilized a user-defined threshold. When predicted power consumption exceeded this threshold, the system autonomously discharged energy from externally distributed resources, including the R-ESS and V2G charger, to alleviate peak loads. During the validation period, the system consistently reduced peak power consumption and reliance on a centralized power supply. In a five-day supervised verification period (8-12 July 2024), the system reduced centralized power demand by 246 kW, achieving a 21.3% reduction in total energy usage. Additionally, the system incorporated a real-time feedback loop, continuously refining the MC-LSTM model by adjusting for discrepancies between predicted and actual consumption. This dynamic calibration process ensured high prediction accuracy and system reliability, even under fluctuating operational conditions. The model's ability to adapt to variations in power consumption ensures its robustness over time, making it suitable for deployment in energy management applications requiring scalability and resilience. Through this case study, the system's practical effectiveness in enhancing grid stability and reducing operational costs was demonstrated, confirming its applicability in real-world energy management scenarios. While our forecasting model demonstrates excellent performance, it is important to note that the energy management system implemented in this study is relatively simple. Figure 7: Comparison of actual and optimized power consumption during peak shaving validation (8–12 July 2024). In our case study, the energy management system served primarily as a tool to validate the forecasting model's performance in a real-world environment rather than as a fully integrated energy scheduling or management system. Its control strategy relies solely on a user-defined peak threshold to trigger charging or discharging actions, without incorporating complex scheduling algorithms. We recognize that integrating more sophisticated energy management system techniques could further enhance system performance and robustness, particularly from a commercialization or business deployment perspective. Consequently, future research will focus on transitioning from fixed, rules-based thresholds to AI-driven adaptive strategies and incorporating weather prediction models along with renewable energy forecasting algorithms. This approach aims to develop more flexible, scalable, and autonomous energy management solutions that dynamically respond to fluctuations in supply and demand. In this way, beyond commercial buildings, the system will possess even greater potential in island regions and off-grid areas where distributed energy resources, such as photovoltaics and wind turbines, are essential for achieving energy self-sufficiency.