Development and comparison of adaptive data-driven models for thermal comfort assessment and control

Thermal comfort prediction is an important issue, as it can largely influence occupants’ well-being and buildings’ energy consumption. Nowadays, models used to assess thermal comfort have been increasingly discussed, and a growing number of data-driven models with several input parameters developed. Although these models allow reasonably accurate predictions of thermal comfort, using complex algorithms to determine thermal comfort might be unsuitable for some use cases, such as quick estimations or real-time control of Heating, Ventilation, and Air Conditioning (HVAC) systems.