Human-in-the-loop methods for occupant-centric building design and operation

A comfortable indoor environment should be one of the main services buildings provide. However, technical building systems are typically designed and operated according to fixed set-point temperatures determined by the ‘one-size-fits-all’ principle assuming universal thermal comfort requirements, which has been questioned in the last fifty years. Designing and implementing comfortable set-point modulations that consider occupant feedback would be beneficial in terms of increasing comfort, potentially reduce energy consumption and significantly support the clean energy transition. An exploratory study aimed at predicting the thermal preferences of human subjects exposed to a dynamic thermal environment is presented. Using data acquired from a laboratory experiment where subjects were exposed to precisely controlled thermal ramps in an ‘office-like’ climatic chamber, cluster-specific and population-averaged methods are designed to handle the group-level residual during the prediction of the thermal preference votes. The results show that both approaches are valid strategies for modelling thermal preference votes and are effective in supporting a concrete occupant-centric building design and the building's operation. Furthermore, the population-averaged approach is suitable for the occupant-centric building design phase, where the target is an ‘average’ occupant. The cluster-specific method is best suited to meet the needs of a specific occupant and is suitable for implementation in the operational phase of the building.