A novel thermal comfort model modified by time scale and habitual trajectory

Reducing the operational energy demand while maintaining a comfortable thermal environment is an essential approach to achieving energy-efficient buildings. The premise lies in accurately assessing and predicting the occupant's thermal sensation. However, most thermal comfort models are not entirely suitable for dwellings, since the effects of time scale, occupant's trajectory, and connectivity between partitioned spaces or rooms in typical dwellings have been ignored. Hence, taking Fanger's model as an example, a modified thermal comfort model involving time scale and habitual trajectory was proposed by changing the mathematical structure, namely the PMVt model. A Python-based visualization program was written to simplify its calculation process. The elderly living in mixed-mode ventilation dwellings in Shanghai were invited to conduct relevant experiments during the summer season. Based on 447 valid thermal sensation votes, weighted and specified indicators corresponding to immediate and delayed inquiries were proposed, respectively. The results show that the PMVt model achieves satisfactory evaluation and prediction accuracy. Moreover, considering time scale and habitual trajectory independently results in a significant reduction in model accuracy, indicating that the synergistic utilization of time scale and trajectory is critical to reducing model errors. Lastly, the applications of the PMVt model in energy-saving strategies for intelligent buildings are prospected, including evaluating thermal comfort, optimizing operation strategy, avoiding energy waste, and reducing energy burden.