A novel model for evaluating dynamic thermal comfort under demand response events

Abstract Smart thermostats are expected to become the first residential appliance to offer significant demand response (DR) capacity worldwide. Their success will depend, to a large extent, on how people's thermal comfort will be affected by the dynamic conditions induced during DR events. To study and evaluate such conditions, researchers have so far mainly relied on Fanger's predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) indices. However, Fanger's model is only suited to predict PMV and PPD under steady-state or slowly changing environmental conditions. For the comfort evaluation of transient thermal conditions, there is still a limited understanding of the psycho-physiological phenomena of thermal alliesthesia and thermal habituation/adaptation, which govern the dynamic thermal perception. In this paper, these two phenomena are incorporated, for the first time, into a dynamic thermal comfort model, which is able to predict the percentage of dissatisfied occupants from Fanger's PMV index. The novel PPD is the result of both a static (PMV-based) and a transient (hedonic and adaptive) component. Since the model builds on the widely-used PMV index, it has the potential to be largely adopted by academics and practitioners and greatly improve their understanding of how people experience comfort and discomfort under DR-induced dynamic environments.