Thermal discomfort prediction with sparse residential thermostat dataset

We develop a probabilistic method for predicting the thermal comfort of residential occupants during demand response (DR) events. Specifically, we estimate the probability that occupants will change the thermostat setpoint, by calculating their discomfort tolerance based on the degree and duration of discomfort. We also show that we can predict this discomfort tolerance reliably.