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Field demonstration of predictive heating control for an all-electric house in a cold climate

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  • Pergantis, Elias N.
  • Priyadarshan,
  • Theeb, Nadah Al
  • Dhillon, Parveen
  • Ore, Jonathan P.
  • Ziviani, Davide
  • Groll, Eckhard A.
  • Kircher, Kevin J.

Abstract

Efficient electric heat pumps that replace fossil-fueled heating systems could significantly reduce greenhouse gas emissions. However, electric heat pumps can sharply increase electricity demand, causing high utility bills and stressing the power grid. Residential neighborhoods could see particularly high electricity demand during cold weather, when heat demand rises and heat pump efficiencies fall. This paper presents the development and field demonstration of a predictive control system for an air-to-air heat pump with backup electric resistance heat. The control system adjusts indoor temperature set-points based on weather forecasts, occupancy conditions, and data-driven models of the building and heating equipment. Field tests from January to March of 2023 in an occupied, all-electric, 208 m2 detached single-family house in Indiana, USA, included outdoor temperatures as low as −15°C. On average over these tests, the control system reduced daily heating energy use by 19% (95% confidence interval: 13%–24%), energy used for backup heat by 38%, and the frequency of using the highest stage (19 kW) of backup heat by 83%. Concurrent surveys of residents showed that the control system maintained satisfactory thermal comfort. The control system could reduce the house’s total annual heating costs by about $300 (95% confidence interval: 23%–34%). These real-world results could strengthen the case for deploying predictive home heating control, bringing the technology one step closer to reducing emissions, utility bills, and power grid impacts at scale.

Suggested Citation

  • Pergantis, Elias N. & Priyadarshan, & Theeb, Nadah Al & Dhillon, Parveen & Ore, Jonathan P. & Ziviani, Davide & Groll, Eckhard A. & Kircher, Kevin J., 2024. "Field demonstration of predictive heating control for an all-electric house in a cold climate," Applied Energy, Elsevier, vol. 360(C).
    • Handle: RePEc:eee:appene:v:360:y:2024:i:c:s0306261924002034
    DOI: 10.1016/j.apenergy.2024.122820
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    References listed on IDEAS

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    1. Pergantis, Elias N. & Reyes Premer, Levi D. & Lee, Alex H. & Priyadarshan, & Liu, Haotian & Groll, Eckhard A. & Ziviani, Davide & Kircher, Kevin J., 2025. "Protecting residential electrical panels and service through model predictive control: A field study," Applied Energy, Elsevier, vol. 386(C).
    2. Panagiotis Michailidis & Iakovos Michailidis & Elias Kosmatopoulos, 2025. "Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations," Energies, MDPI, vol. 18(7), pages 1-40, March.

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