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Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

Author

Listed:
  • Jaewan Joe

    (Department of Architectural Engineering, INHA University, Inha-ro 100, Nam-gu, Incheon 22212, Korea)

  • Piljae Im

    (Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA)

  • Jin Dong

    (Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA)

Abstract

This study provides a general procedure to generate a direct expansion (DX) cooling coil system for a roof top unit (RTU), which is a typical heating ventilation and air-conditioning (HVAC) system for commercial buildings in the United States. Experimental data from a full-scale unoccupied 2-story commercial building is used for the HVAC modeling. The regression for identifying the model coefficients was carried out with multiple stages, and the results were validated with measured data. The model’s applicability was evaluated with multiple case studies, including a building energy simulation (BES) program validation, model-based predictive control (MPC), and fault diagnostics and detection (FDD).

Suggested Citation

  • Jaewan Joe & Piljae Im & Jin Dong, 2020. "Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8738-:d:432568
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    References listed on IDEAS

    as
    1. Byung-Ki Jeon & Eui-Jong Kim & Younggy Shin & Kyoung-Ho Lee, 2018. "Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems," Sustainability, MDPI, vol. 11(1), pages 1-16, December.
    2. Joe, Jaewan & Karava, Panagiota, 2019. "A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings," Applied Energy, Elsevier, vol. 245(C), pages 65-77.
    3. Younès Dagdougui & Ahmed Ouammi & Rachid Benchrifa, 2020. "Energy Management-Based Predictive Controller for a Smart Building Powered by Renewable Energy," Sustainability, MDPI, vol. 12(10), pages 1-18, May.
    4. Im, Piljae & Joe, Jaewan & Bae, Yeonjin & New, Joshua R., 2020. "Empirical validation of building energy modeling for multi-zones commercial buildings in cooling season," Applied Energy, Elsevier, vol. 261(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Byung-Ki Jeon & Eui-Jong Kim, 2021. "LSTM-Based Model Predictive Control for Optimal Temperature Set-Point Planning," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    2. Joe, Jaewan & Im, Piljae & Cui, Borui & Dong, Jin, 2023. "Model-based predictive control of multi-zone commercial building with a lumped building modelling approach," Energy, Elsevier, vol. 263(PA).

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