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Scalable identification and control of residential heat pumps: A minimal hardware approach

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  • Lee, Zachary E.
  • Zhang, K. Max

Abstract

While model predictive control (MPC) is a widely studied tool for integrating residential heat pumps with the smart grid, modeling difficulties and hardware requirements are significant barriers to adoption. In response, we present a nonintrusive plug-and-play methodology for model identification and model predictive control using only two increasingly popular smart-home devices: a smart thermostat and smart electricity meter, which makes the overall approach highly scalable. However, this minimal hardware approach requires new methods to overcome modeling challenges due to the lack of data diversity. A data-driven heat pump power model is identified without the need for submetering by using energy disaggregation. By incorporating the heat pump control data from the smart thermostat, we provide implicit load classification data to the model, substantially simplifying the signal decomposition task. Building and heat pump model parameters are then identified by a novel algorithm that combats overfitting caused by limited state excitation. Finally, we show the advantage of this scalable approach in an aggregate model predictive controller enabled by the communication ability provided by the smart thermostat. Results show that this aggregate control approach can provide improved grid services, such as reduced peak demand, while at the same time reducing energy consumption and improving thermal comfort. The proposed method has the potential to greatly lower the barrier for residential energy aggregation.

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  • 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).
    • Handle: RePEc:eee:appene:v:286:y:2021:i:c:s0306261921000945
    DOI: 10.1016/j.apenergy.2021.116544
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    References listed on IDEAS

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    1. Zhang, Lingxi & Good, Nicholas & Mancarella, Pierluigi, 2019. "Building-to-grid flexibility: Modelling and assessment metrics for residential demand response from heat pump aggregations," Applied Energy, Elsevier, vol. 233, pages 709-723.
    2. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    3. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    4. Baeten, Brecht & Confrey, Thomas & Pecceu, Sébastien & Rogiers, Frederik & Helsen, Lieve, 2016. "A validated model for mixing and buoyancy in stratified hot water storage tanks for use in building energy simulations," Applied Energy, Elsevier, vol. 172(C), pages 217-229.
    5. 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.
    6. Clauß, John & Georges, Laurent, 2019. "Model complexity of heat pump systems to investigate the building energy flexibility and guidelines for model implementation," Applied Energy, Elsevier, vol. 255(C).
    7. Chua, K.J. & Chou, S.K. & Yang, W.M., 2010. "Advances in heat pump systems: A review," Applied Energy, Elsevier, vol. 87(12), pages 3611-3624, December.
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    Cited by:

    1. Lee, Zachary E. & Zhang, K. Max, 2021. "Generalized reinforcement learning for building control using Behavioral Cloning," Applied Energy, Elsevier, vol. 304(C).
    2. Amadeh, Ali & Lee, Zachary E. & Zhang, K. Max, 2022. "Quantifying demand flexibility of building energy systems under uncertainty," Energy, Elsevier, vol. 246(C).
    3. Brudermueller, Tobias & Kreft, Markus & Fleisch, Elgar & Staake, Thorsten, 2023. "Large-scale monitoring of residential heat pump cycling using smart meter data," Applied Energy, Elsevier, vol. 350(C).
    4. Shao, Junqiang & Huang, Zhiyuan & Chen, Yugui & Li, Depeng & Xu, Xiangguo, 2023. "A practical application-oriented model predictive control algorithm for direct expansion (DX) air-conditioning (A/C) systems that balances thermal comfort and energy consumption," Energy, Elsevier, vol. 269(C).
    5. Lee, Zachary E. & Zhang, K. Max, 2023. "Regulated peer-to-peer energy markets for harnessing decentralized demand flexibility," Applied Energy, Elsevier, vol. 336(C).

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