Abstract
This study aims to propose Real-COMFORT - a novel occupant-centered real-time indoor temperature control system using deep learning algorithms to simultaneously optimize the thermal comfort and energy consumption of individual occupants. Based on chamber experiments with wireless sensor networks, one-dimensional convolutional neural networks (1D CNN)-based model was developed for automated recognition of occupant activity, and a data-efficient RL-based model was developed for indoor temperature control. The results showed that the proposed system could automatically control the indoor temperature in real time by reducing by 10.9% the thermal discomfort of the occupants with different thermal sensation characteristics and physical activities while maintaining their energy consumption. The proposed system could provide the ultimate occupant-centered indoor temperature control reflecting the dynamic and personalized characteristics of recent smart building control systems.
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