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Optimal Control of Air-Side Economizer

Author

Listed:
  • Jin-Hyun Lee

    (Architecture Research Institute, Yeungnam University, Gyeongsan 38541, Republic of Korea)

  • Young-Hum Cho

    (School of Architecture, Yeungnam University, Gyeongsan 38541, Republic of Korea)

Abstract

The economizer system is a method of improving energy efficiency through the operating method, which introduces outdoor air through dampers when the outdoor air temperature or enthalpy is lower than the that of the indoor air. The set values used for economizer control include the mixed air temperature and high and low limits. The set values are presented as fixed values in the relevant standards and are controlled to be fixed during actual operation, which may lead to issues such as indoor discomfort, poor indoor air quality, and energy wastage. Therefore, it is necessary to optimize economizer control by determining appropriate set values considering the indoor and outdoor environments. To this end, this paper reviewed the economizer system, control method, control set values, and prediction models in buildings. As a result, it was concluded that optimal economizer control is possible by utilizing a prediction model.

Suggested Citation

  • Jin-Hyun Lee & Young-Hum Cho, 2024. "Optimal Control of Air-Side Economizer," Energies, MDPI, vol. 17(21), pages 1-12, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5383-:d:1509342
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    References listed on IDEAS

    as
    1. Yu-Jin Kim & Kwang-Hee Kim & Ju-Wan Ha & Young-Hak Song, 2024. "Research on a Plan of Free Cooling Operation Control for the Efficiency Improvement of a Water-Side Economizer," Energies, MDPI, vol. 17(12), pages 1-17, June.
    2. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    3. Xu, Yuanjin & Li, Fei & Asgari, Armin, 2022. "Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms," Energy, Elsevier, vol. 240(C).
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