Research Topics on Occupant-Centred Space Heating Control Systems
Adaptive Multi-Target Localization for Occupant Tracking
Accurate occupant localization is central to personalized climate control. This study employs an adaptive multi-target localization method using the Density Peak Clustering (DPC) algorithm to detect real-time occupant positions. The algorithm’s strength lies in its ability to identify distinct data clusters without prior assumptions about the number of occupants. This innovation enables precise tracking in complex indoor environments, forming the basis for temperature control that directly responds to occupants’ actual locations.
Development of a 3D Multiphysics Thermal Model
A three-dimensional Multiphysics thermal model is developed to simulate spatial indoor temperature distributions. This model integrates heat transfer mechanisms, including conduction, convection, and radiation, to predict temperature variations with high accuracy. The predicted data provides localized temperature information surrounding each occupant, allowing the control system to optimize heating output in real time. Experimental validation demonstrates that the model maintains an average relative error below 1%, highlighting its robustness and reliability for dynamic indoor environments.
Hierarchical State Machine (HSM) Control Framework
The control mechanism is structured around a Hierarchical State Machine (HSM) that organizes the decision-making process into layered states. This architecture allows for flexible and efficient control transitions based on feedback from localized temperature data. The HSM framework optimizes control actions dynamically, adapting to occupant movement and environmental fluctuations. This structured approach ensures stability, adaptability, and scalability in real-world applications of intelligent heating systems.
Energy Efficiency and Thermal Comfort Improvements
Experimental results show that the proposed occupant-centred heating control strategy can reduce energy consumption by over 20% compared to conventional methods. At the same time, thermal comfort levels—quantified through user feedback and temperature uniformity—improve by up to 38%. These results demonstrate the potential of spatially aware, occupant-based heating systems to contribute significantly to sustainable building operations, particularly in smart homes and intelligent building management systems.
Future Prospects and Research Directions
The proposed control framework opens avenues for further research in intelligent environmental management. Future studies could explore integrating additional sensory data such as humidity, air quality, and occupant activity levels to refine comfort prediction. Moreover, combining this approach with renewable energy systems and Internet of Things (IoT) networks could enable fully autonomous, self-learning climate systems. Continued advancements in computational models and sensor fusion will further strengthen occupant-centric design principles in next-generation building technologies.
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