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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月10日

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  • SungKu Kang,
  • Maharshi Pathak,
  • Kunind Sharma,
  • Emily Casavant,
  • Katherine Bassett,
  • Misha Pavel,
  • David Fannon,
  • Michael Kane

摘要

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.