Review on occupant-centric thermal comfort sensing, predicting, and controlling

Ensuring occupants' thermal comfort and work performance is one of the primary objectives for building environment conditioning systems. In recent years, there emerged many occupant-orientated technologies aiming to optimize thermal comfort while saving energy. These attempts offered opportunities to move the indoor thermal environment control from the one-fits-all approach toward a new paradigm with occupant-centric merits. A timely review of this emerging field would help to fill the knowledge gap and provide new insights for future research and practice. This study performed a literature review to summarize recent occupant-centric thermal comfort practices following a framework with three themes: sensing, predicting, and controlling. The results show that occupant-centric thermal comfort control has become a hot research topic in recent years. A wide range of variables and data-collecting sensors were utilized to support the concept. Among all the potential variables, occupants' comfort feedback, skin temperature, and air temperature are the top three popular input features for thermal comfort prediction. Using different machine learning algorithms, data-driven thermal comfort models were reported to have a median predicting accuracy of 84% and some of them can predict thermal comfort at a personal level. Cases implementing occupant-centric thermal comfort control strategy were reported to save air-conditioning energy by 22% and improve thermal comfort by 29.1%. These observations from the literature support the prospects of the new thermal comfort paradigm. Additionally, the challenges and opportunities in this emerging field were discussed. (C) 2020 Elsevier B.V. All rights reserved.