Given the critical role of indoor thermal comfort in occupants’ well-being and productivity, this study conducted preliminary research to develop a framework for energy management strategies that emphasize thermal comfort and seasonal variations in energy consumption from heating, ventilation, and air conditioning (HVAC) systems. Using a kindergarten facility in South Korea as a case study, the research collected real-time data on indoor thermal conditions and energy usage over one year through an IoT-based sensor network. K-Means clustering was employed to identify distinct energy consumption patterns. The main findings are as follows: First, the cluster analysis identified three significant patterns corresponding to seasonal variations—Cluster 1 representing summer, Cluster 2 representing winter, and Cluster 3 representing the transitional periods of spring and fall. Second, tailored energy benchmark values were established for each cluster, ensuring alignment with seasonal demand while maintaining optimal thermal comfort for occupants. The analysis also revealed the gap between benchmark values and current energy usage, highlighting the significant potential for energy savings. This study demonstrates the feasibility of achieving energy savings in HVAC systems without compromising occupants’ thermal comfort. By offering a scalable framework adaptable to various building types, this research lays the foundation for efficient energy management practices that can enhance occupants’ thermal comfort and reduce energy consumption.