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Field experiment testing of a low-cost model predictive controller (MPC) for building heating systems and analysis of phase change material (PCM) integration

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  • Wei, Zhichen
  • Calautit, John Kaiser

Abstract

Model Predictive Control (MPC) emerges as a promising solution to address the substantial greenhouse gas emissions from the building sector. By employing advanced control strategies, such as MPC, for peak energy shifting, there is a significant potential to enhance energy efficiency and reduce emissions through effective demand response within a smart grid. Although MPC has been the subject of extensive research, the practical implementation of cost-effective and easily deployable solutions in buildings remains limited. This study proposes a cost-effective MPC approach, employing the Internet of Things (IoT) and dynamic pricing. The control strategy, developed in MATLAB, is locally deployed on Raspberry Pi hardware via WiFi. The proposed MPC was tested in a controlled environment at the University of Nottingham, UK, where it regulated a radiator heating device to maintain indoor comfort in response to dynamic hourly electricity prices, using real-time indoor temperature feedback. The results confirmed the proposed MPC's accuracy in predicting indoor temperature responses and controlling indoor temperature within setpoints over a typical winter week. Performance analysis further revealed that the proposed MPC strategy resulted in a 20% electricity cost reduction compared to a conventional control strategy. Additionally, alongside the proposed MPC strategy, a Phase Change Material (PCM) wallboard system was integrated into a co-simulation platform. The developed PCM wallboard model underwent a verification and validation process, utilizing both numerical simulations and experimental data. The results demonstrate that the proposed MPC-controlled PCM wallboard system saved 35% on electricity cost compared with the original case study room. This study provides valuable insights into the development of intelligent localized demand response control for the built environment, offering a range of choices for IoT equipment.

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  • Wei, Zhichen & Calautit, John Kaiser, 2024. "Field experiment testing of a low-cost model predictive controller (MPC) for building heating systems and analysis of phase change material (PCM) integration," Applied Energy, Elsevier, vol. 360(C).
  • Handle: RePEc:eee:appene:v:360:y:2024:i:c:s0306261924001338
    DOI: 10.1016/j.apenergy.2024.122750
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    References listed on IDEAS

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    1. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. Wei, Zhichen & Calautit, John, 2023. "Predictive control of low-temperature heating system with passive thermal mass energy storage and photovoltaic system: Impact of occupancy patterns and climate change," Energy, Elsevier, vol. 269(C).
    3. Wang, Huakeer & Lu, Wei & Wu, Zhigen & Zhang, Guanhua, 2020. "Parametric analysis of applying PCM wallboards for energy saving in high-rise lightweight buildings in Shanghai," Renewable Energy, Elsevier, vol. 145(C), pages 52-64.
    4. Hannon, Matthew J., 2015. "Raising the temperature of the UK heat pump market: Learning lessons from Finland," Energy Policy, Elsevier, vol. 85(C), pages 369-375.
    5. de Gracia, Alvaro & Tarragona, Joan & Crespo, Alicia & Fernández, Cèsar, 2020. "Smart control of dynamic phase change material wall system," Applied Energy, Elsevier, vol. 279(C).
    6. Langer, Lissy & Volling, Thomas, 2020. "An optimal home energy management system for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 278(C).
    7. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics," Applied Energy, Elsevier, vol. 326(C).
    9. Mouli-Castillo, Julien & Heinemann, Niklas & Edlmann, Katriona, 2021. "Mapping geological hydrogen storage capacity and regional heating demands: An applied UK case study," Applied Energy, Elsevier, vol. 283(C).
    10. 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).
    11. Kishore, Ravi Anant & Bianchi, Marcus V.A. & Booten, Chuck & Vidal, Judith & Jackson, Roderick, 2021. "Enhancing building energy performance by effectively using phase change material and dynamic insulation in walls," Applied Energy, Elsevier, vol. 283(C).
    12. Khodabakhshian, Mohammad & Feng, Lei & Börjesson, Stefan & Lindgärde, Olof & Wikander, Jan, 2017. "Reducing auxiliary energy consumption of heavy trucks by onboard prediction and real-time optimization," Applied Energy, Elsevier, vol. 188(C), pages 652-671.
    13. Thi Kim Tuoi, Truong & Van Toan, Nguyen & Ono, Takahito, 2022. "Self-powered wireless sensing system driven by daily ambient temperature energy harvesting," Applied Energy, Elsevier, vol. 311(C).
    14. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2021. "Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems," Applied Energy, Elsevier, vol. 297(C).
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