Framework of Occupant-Centric Measuring System for Personalized Micro-environment via Online Modeling

Indoor environmental comfort has become increasingly important, necessitating occupant-centric systems that provide personalized comfort. This trend is particularly notable in light of the increasing frequency of extreme weather events associated with global climate change. This paper proposes a novel framework integrating real-time occupant feedback, multi-sensor data fusion, online modeling, and intelligent sensor technologies to dynamically tailor indoor micro-environments. The framework collects diverse data on built environment and personal health using environmental sensors and wearable devices. It employs online machine learning algorithms to analyze the database and automatically adjust environmental conditions in real-time to match occupants’ preferences. In implementing this framework, advanced encryption are utilized to enable swift, localized data processing while preserving privacy. Multi-sensor fusion techniques are leveraged to integrate heterogeneous sensor data into an accurate assessment of occupant comfort. The user interface facilitates occupant feedback to continuously refine the system’s reinforcement learning model. By personalizing comfort in a responsive, privacy-aware manner, this framework is expected to enhance occupant well-being and satisfaction, potentially enabling significant energy savings by avoiding overcooling and overheating. The framework represents an innovative application of smart and computing technologies, including deep learning and data fusion, to advance beyond static environmental setpoints. In anticipation of testing, it shows promise in revolutionizing occupant-centric comfort, fostering the creation of more adaptive and resilient indoor spaces.