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Model predictive control for supervising multiple rooftop unit economizers to fully leverage free cooling energy resource

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  • Kim, Donghun
  • Braun, James E.

Abstract

“Free cooling” with cool outside air can meet or reduce a building’s cooling loads in an energy efficient manner when conditions are favorable. For rooftop air conditioning units (RTUs), this is achieved with air-side economizers that are integrated with the packaged RTU. However, current RTU economizers are independently controlled at the unit level and may not fully leverage the free resource for economic benefit especially when multiple units serve a common zone of a building, such as in retail stores. That is, the independent control of multiple RTUs could lead to some units operating with mechanical cooling even when the outdoor air temperature or enthalpy is low enough to meet the load with economizer cooling. This paper presents a model-predictive control (MPC) approach to optimally supervise multiple RTUs in order to fully utilize the free cooling resource. The algorithm is applied to the evaluation of the energy savings potential for a case study building served by four identical RTUs under various climate zones over a cooling season.

Suggested Citation

  • Kim, Donghun & Braun, James E., 2020. "Model predictive control for supervising multiple rooftop unit economizers to fully leverage free cooling energy resource," Applied Energy, Elsevier, vol. 275(C).
    • Handle: RePEc:eee:appene:v:275:y:2020:i:c:s0306261920308369
    DOI: 10.1016/j.apenergy.2020.115324
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    References listed on IDEAS

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    4. Zhang, Xiangyu & Pipattanasomporn, Manisa & Rahman, Saifur, 2017. "A self-learning algorithm for coordinated control of rooftop units in small- and medium-sized commercial buildings," Applied Energy, Elsevier, vol. 205(C), pages 1034-1049.
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    Cited by:

    1. Goopyo Hong & Chul Kim & Jun Hong, 2020. "Energy Conservation Potential of Economizer Controls Using Optimal Outdoor Air Fraction Based on Field Study," Energies, MDPI, vol. 13(19), pages 1-19, September.
    2. Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "Quantum computing for future real-time building HVAC controls," Applied Energy, Elsevier, vol. 334(C).

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