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Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends

Author

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  • Dongsu Kim

    (Department of Architectural Engineering, Hanbat National University, Daejeon 34158, Korea)

  • Jongman Lee

    (Department of Architecture, College of Engineering, Korea University, Seoul 02841, Korea)

  • Sunglok Do

    (Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea)

  • Pedro J. Mago

    (Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506, USA)

  • Kwang Ho Lee

    (Department of Architecture, College of Engineering, Korea University, Seoul 02841, Korea)

  • Heejin Cho

    (Department of Mechanical Engineering, Mississippi State University, Starkville, MS 39759, USA)

Abstract

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems’ performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI’s and Elsevier’s databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields.

Suggested Citation

  • Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7231-:d:931369
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    3. Hyang-A Park & Gilsung Byeon & Wanbin Son & Jongyul Kim & Sungshin Kim, 2023. "Data-Driven Modeling of HVAC Systems for Operation of Virtual Power Plants Using a Digital Twin," Energies, MDPI, vol. 16(20), pages 1-14, October.
    4. Stefano Converso & Paolo Civiero & Stefano Ciprigno & Ivana Veselinova & Saffa Riffat, 2023. "Toward a Fast but Reliable Energy Performance Evaluation Method for Existing Residential Building Stock," Energies, MDPI, vol. 16(9), pages 1-24, May.

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