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Predictive Heating Control and Perceived Thermal Comfort in a Norwegian Office Building

Author

Listed:
  • Nicola Lolli

    (SINTEF Community—Architecture, Materials and Structures, 7465 Trondheim, Norway)

  • Evgenia Gorantonaki

    (SINTEF Community—Architecture, Materials and Structures, 7465 Trondheim, Norway)

  • John Clauß

    (SINTEF Community—Architecture, Materials and Structures, 7465 Trondheim, Norway)

Abstract

An office building in Trondheim, Norway, was used as a case study to test the influence of Predictive Control (PC) for the optimization of energy use on the employees’ thermal comfort. A predictive control was implemented in the Building Energy Management System (BEMS) by operating on the supply temperature of the radiator circuit. A questionnaire was given to the employees to evaluate to what extent the operation of the predictive control influenced their perceived thermal comfort. Several factors known to influence employees’ satisfaction (such as office type, perceived noise level, level of control, perceived luminous environment, perceived indoor air quality, adaptation strategies, well-being) were investigated in the questionnaire. The evaluation shows that the occupants rated the perceived thermal comfort as equally good compared to the business-as-usual operation. This is an important finding toward the user acceptance of such predictive control schemes.

Suggested Citation

  • Nicola Lolli & Evgenia Gorantonaki & John Clauß, 2024. "Predictive Heating Control and Perceived Thermal Comfort in a Norwegian Office Building," Energies, MDPI, vol. 17(15), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3719-:d:1444768
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    References listed on IDEAS

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    1. Hua, Pengmin & Wang, Haichao & Xie, Zichan & Lahdelma, Risto, 2024. "Multi-criteria evaluation of novel multi-objective model predictive control method for indoor thermal comfort," Energy, Elsevier, vol. 289(C).
    2. Liu, Zhikai & Zhang, Huang & Wang, Yaran & Fan, Xianwang & You, Shijun & Li, Ang, 2023. "Data-driven predictive model for feedback control of supply temperature in buildings with radiator heating system," Energy, Elsevier, vol. 280(C).
    3. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    4. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    5. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "Energy saving and indoor temperature control for an office building using tube-based robust model predictive control," Applied Energy, Elsevier, vol. 341(C).
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