Work

Thermal discomfort prediction with sparse residential thermostat dataset

Year: 2023

Type: conference-paper

Abstract: 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.

Source: ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)

DOI: 10.1145/3600100.3626280

Authors: Hannah Fontenot, Michael Zeifman, Kurt Roth

Institution: Fraunhofer USA Center for Manufacturing Innovation

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: bronze

Funders: U.S. Department of Energy, Universitas Brawijaya