# Longitudinal monitoring of twenty homes reveals spatiotemporal dynamics which require new models of discomfort and thermostat use

## TL;DR

A study of 20 homes found that current models for predicting thermal comfort are inaccurate due to spatiotemporal temperature variations, suggesting a need for new approaches to manage energy demand.

## Contribution

The study introduces the largest HBI dataset and reveals that existing thermal comfort models fail under real-world spatiotemporal variations, advocating for new models of discomfort.

## Key findings

- Current thermal comfort models show greater error when temperature variations exceed 2°F.
- Mean spatial temperature variation within homes was 4°F, suggesting limitations in existing models.
- Thermostat-based demand response can worsen temperature fluctuations, highlighting the need for improved algorithms.

## Abstract

Growing variable renewable energy and electrification of heating and transportation are intensifying the challenge of operating the electric grid. However, current demand response (DR) approaches compromise their efficacy by neglecting human-building interactions (HBIs). For example, utilities may increase thermostat setpoints on the hottest days of the year, reducing the strain on the grid but making occupants uncomfortable and frustrated. To better understand HBIs in residential buildings, 41 people in 20 homes in two climates participated in a 6-month study. Timestamps from app-based thermal comfort surveys and thermostat interactions were synchronized to time-series building systems data, resulting in the largest-of-its-kind HBI dataset. These survey data are compared to predictions from industry-standard thermal comfort models. Our analysis found that these models, developed under steady-state assumptions, tend to yield greater error magnitudes and/or biases when spatiotemporal temperature variations exceed 2°F, with several comparisons reaching statistical significance. The mean spatial variation within homes in the dataset was 4°F. Thermostat DR control would commonly exacerbate such temporal variation. The results highlight opportunities for improving DR load-control algorithms through a paradigm shift to modeling discomfort rather than comfort, increasing the use of low-cost sensors, and incorporating dynamic models of occupant behavior.

The online version contains supplementary material available at 10.1038/s41598-025-32727-y.