## 4 Discussion  
This study conducted a parametric study based on an energy model along with an experimental analysis of occupancy status and activities in predefined spatial zones. By defining activities in residential buildings considering furniture placement and utilizing the characteristics of FMCW radar sensors for pre-zoning, this study developed and evaluated operation scenarios for a hydronic radiant panel heating system based on occupancy patterns. Microcontrollers and home automation systems were used to automatically acquire data on occupied spaces, and schedules for sleep, daily activities, and unoccupied states were created based on the stored sensor data. The potential for varying set temperatures according to these schedules was evaluated in terms of thermal comfort and energy consumption using an energy simulation model. The results of this study can be linked to those of previous research. Compared to previous studies on occupancy presence sensors [16,18] and tracking individual occupant movements [14], our approach offers several advantages: cost-effective wireless sensor stations, enhanced privacy through non-terminal occupancy detection, and the ability to infer activities within occupied zones based on spatial characteristics. While our method requires additional effort to cluster indoor space dimensions and furniture locations, it may also necessitate system integration with conventional thermostats for controlling heating systems based on occupant activity. Additionally, our approach may require multiple sensors to cover large spaces due to the limited detection range of 6 m.  
Additionally, compared to office buildings where occupant indoor activities are relatively simple and the number of occupants is a primary concern, this study can be linked to systems like [26] to implement occupant-based optimal control for residential buildings where various activities such as sleep, daily work, study, and others take place. By linking it to methods for evaluating the correlation between the age of a residential building and its energy consumption [24], a comprehensive analysis model can be developed that considers occupant activities and performance degradation due to the building's age. From a policy perspective, when developing reference energy models for specific regions and building stocks, providing time-based occupancy status and activity information along with single activity level and clothing level indicators [44] can minimize the gap between actual building energy consumption and the results of energy analysis models, reflecting real-world data in building energy standards. To enhance the generalizability and practicality of the research findings, the following additional studies will be conducted: First, research will be conducted to improve the reliability of the sensor system. The current single occupancy sensor and software-based space division may lead to erroneous judgments when there is no movement for a certain period. Therefore, it is necessary to use various sensors or improve the occupancy detection algorithm to obtain more accurate occupancy information. Second, the integration with home automation systems will be strengthened. The system developed in this study has shown the potential to effectively control indoor temperature and ensure comfort. Based on this, the performance of the system will be verified in a real environment through direct connection with heating water control valves, and optimal control strategies will be developed by applying various scenarios. Third, verification in various spaces and building types is necessary. Performance verification has thus far focused on individual spaces like bedrooms. To gain a more comprehensive understanding, it should evaluate the energy model and system operation in various settings, including total residential units