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.

In this paper, a data-driven model was developed based on 61710 samples of subjective responses associated with environmental parameters from field studies available in two ASHRAE databases. Two models resulted from this analysis, one with higher accuracy and one simplified, which improved the prediction in comparison to other regression models and PMV.

However, since thermal comfort cannot be conceived as a punctual condition, comfort areas were derived, i.e., respective comfort ranges at 90%, 80%, and 70% of thermal acceptability. The result is that the error in the prediction of the new models is below the 90% acceptable range, which means that the models' error does not lead to a reduction in the evaluation of occupant comfort.

Built upon influential parameters, these models enable thermal comfort estimates and occupant-centered HVAC control. The notion of comfort as a non-fixed state empowers more flexible building management criteria, reducing energy use while upholding indoor comfort.