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Field demonstration and implementation analysis of model predictive control in an office HVAC system

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  • Blum, David
  • Wang, Zhe
  • Weyandt, Chris
  • Kim, Donghun
  • Wetter, Michael
  • Hong, Tianzhen
  • Piette, Mary Ann

Abstract

Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:318:y:2022:i:c:s0306261922004895
    DOI: 10.1016/j.apenergy.2022.119104
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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. 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.
    3. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2019. "Data fusion in predicting internal heat gains for office buildings through a deep learning approach," Applied Energy, Elsevier, vol. 240(C), pages 386-398.
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    Citations

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    Cited by:

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    2. Jiang, Yuliang & Zhu, Shanying & Xu, Qimin & Yang, Bo & Guan, Xinping, 2023. "Hybrid modeling-based temperature and humidity adaptive control for a multi-zone HVAC system," Applied Energy, Elsevier, vol. 334(C).
    3. Chen, Yibo & Gao, Junxi & Yang, Jianzhong & Berardi, Umberto & Cui, Guoyou, 2023. "An hour-ahead predictive control strategy for maximizing natural ventilation in passive buildings based on weather forecasting," Applied Energy, Elsevier, vol. 333(C).
    4. Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
    5. 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).
    6. Leonidas Zouloumis & Angelos Karanasos & Nikolaos Ploskas & Giorgos Panaras, 2023. "Multicriteria Design and Operation Optimization of a Solar-Assisted Geothermal Heat Pump System," Energies, MDPI, vol. 16(3), pages 1-16, January.
    7. Nweye, Kingsley & Sankaranarayanan, Siva & Nagy, Zoltan, 2023. "MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities," Applied Energy, Elsevier, vol. 346(C).

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