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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. Lee, Zachary E. & Zhang, K. Max, 2021. "Scalable identification and control of residential heat pumps: A minimal hardware approach," Applied Energy, Elsevier, vol. 286(C).
    2. Kuldeep Kurte & Jeffrey Munk & Olivera Kotevska & Kadir Amasyali & Robert Smith & Evan McKee & Yan Du & Borui Cui & Teja Kuruganti & Helia Zandi, 2020. "Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    3. Kircher, Kevin J. & Zhang, K. Max, 2021. "Heat purchase agreements could lower barriers to heat pump adoption," Applied Energy, Elsevier, vol. 286(C).
    4. Beccali, Marco & Bonomolo, Marina & Martorana, Francesca & Catrini, Pietro & Buscemi, Alessandro, 2022. "Electrical hybrid heat pumps assisted by natural gas boilers: a review," Applied Energy, Elsevier, vol. 322(C).
    5. Song, Mengjie & Deng, Shiming & Dang, Chaobin & Mao, Ning & Wang, Zhihua, 2018. "Review on improvement for air source heat pump units during frosting and defrosting," Applied Energy, Elsevier, vol. 211(C), pages 1150-1170.
    6. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    7. Blum, David & Wang, Zhe & Weyandt, Chris & Kim, Donghun & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Field demonstration and implementation analysis of model predictive control in an office HVAC system," Applied Energy, Elsevier, vol. 318(C).
    8. Brown, Sarah & Beausoleil-Morrison, Ian, 2023. "Long-term implementation of a model predictive controller for a hydronic floor heating and cooling system in a highly glazed house in Canada," Applied Energy, Elsevier, vol. 349(C).
    9. Knudsen, Michael Dahl & Georges, Laurent & Skeie, Kristian Stenerud & Petersen, Steffen, 2021. "Experimental test of a black-box economic model predictive control for residential space heating," Applied Energy, Elsevier, vol. 298(C).
    10. Langer, Lissy & Volling, Thomas, 2020. "An optimal home energy management system for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 278(C).
    11. Gasser, Jan & Cai, Hanmin & Karagiannopoulos, Stavros & Heer, Philipp & Hug, Gabriela, 2021. "Predictive energy management of residential buildings while self-reporting flexibility envelope," Applied Energy, Elsevier, vol. 288(C).
    12. Blum, D.H. & Arendt, K. & Rivalin, L. & Piette, M.A. & Wetter, M. & Veje, C.T., 2019. "Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems," Applied Energy, Elsevier, vol. 236(C), pages 410-425.
    13. Bünning, Felix & Huber, Benjamin & Schalbetter, Adrian & Aboudonia, Ahmed & Hudoba de Badyn, Mathias & Heer, Philipp & Smith, Roy S. & Lygeros, John, 2022. "Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC," Applied Energy, Elsevier, vol. 310(C).
    14. Ma, Jiacheng & Kim, Donghun & Braun, James E. & Horton, W. Travis, 2023. "Development and validation of a dynamic modeling framework for air-source heat pumps under cycling of frosting and reverse-cycle defrosting," Energy, Elsevier, vol. 272(C).
    15. Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
    16. Salpakari, Jyri & Lund, Peter, 2016. "Optimal and rule-based control strategies for energy flexibility in buildings with PV," Applied Energy, Elsevier, vol. 161(C), pages 425-436.
    17. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
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