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Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller

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  • Jonghoon Ahn

    (School of Architecture and Design Convergence, Hankyong National University, Anseong 17579, Korea)

Abstract

Various methods to control thermal conditions of building spaces have been developed to investigate their performances of energy use and thermal comfort in the system levels. However, the high control precision used in several studies dealing with data-driven methods may cause energy increases and the high energy efficiency may be disadvantageous for maintaining indoor environmental quality. This study proposes a model that optimizes the supply air condition to effectively reach the setting values by two-way controls of the supply air conditions. In such a process, if the results of the thermal comfort level are outside the range of the initial setting values, an adaptive model starts to work to send additional signals to adjust the set-point temperature. In order to assess its efficiency, the conventional thermostat model and fuzzy deterministic model are adopted as comparators. Comparing the results of the proposed network-based model with conventional control models, an improved control performance from 15.5% to 29.3% in thermal comfort indices was identified, as well as an over 30% improvement in energy efficiency. As a consequence, the network-based adaptive control rule supervising thermal comfort indices properly operates to abate increases in its energy use without compromising its thermal comfort. This performance can be significant in places where many spaces are woven at high density, and in situations where better thermal comfort can increase users’ workability and productivity.

Suggested Citation

  • Jonghoon Ahn, 2021. "Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1353-:d:488481
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    References listed on IDEAS

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    1. Bożena Babiarz & Władysław Szymański, 2020. "Introduction to the Dynamics of Heat Transfer in Buildings," Energies, MDPI, vol. 13(23), pages 1-28, December.
    2. Kuldeep Kurte & Jeffrey Munk & Olivera Kotevska & Kadir Amasyali & Robert Smith & Evan McKee & Yan Du & Borui Cui & Teja Kuruganti & Helia Zandi, 2020. "Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    3. Jonghoon Ahn, 2020. "Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    4. Ren, Zhengen & Chen, Dong, 2018. "Modelling study of the impact of thermal comfort criteria on housing energy use in Australia," Applied Energy, Elsevier, vol. 210(C), pages 152-166.
    5. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    6. Sung Hoon Yoon & Jonghoon Ahn, 2020. "Comparative Analysis of Energy Use and Human Comfort by an Intelligent Control Model at the Change of Season," Energies, MDPI, vol. 13(22), pages 1-15, November.
    7. Branko Simanic & Birgitta Nordquist & Hans Bagge & Dennis Johansson, 2020. "Influence of User-Related Parameters on Calculated Energy Use in Low-Energy School Buildings," Energies, MDPI, vol. 13(11), pages 1-14, June.
    8. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
    9. Hu, Shushan & Hoare, Cathal & Raftery, Paul & O’Donnell, James, 2019. "Environmental and energy performance assessment of buildings using scenario modelling and fuzzy analytic network process," Applied Energy, Elsevier, vol. 255(C).
    10. Spandagos, Constantine & Ng, Tze Ling, 2018. "Fuzzy model of residential energy decision-making considering behavioral economic concepts," Applied Energy, Elsevier, vol. 213(C), pages 611-625.
    11. Singh, Manoj Kumar & Attia, Shady & Mahapatra, Sadhan & Teller, Jacques, 2016. "Assessment of thermal comfort in existing pre-1945 residential building stock," Energy, Elsevier, vol. 98(C), pages 122-134.
    12. Ahn, Jonghoon & Chung, Dae Hun & Cho, Soolyeon, 2018. "Energy cost analysis of an intelligent building network adopting heat trading concept in a district heating model," Energy, Elsevier, vol. 151(C), pages 11-25.
    13. Ahn, Jonghoon & Cho, Soolyeon, 2017. "Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments," Applied Energy, Elsevier, vol. 204(C), pages 117-130.
    14. Ahn, Jonghoon & Cho, Soolyeon & Chung, Dae Hun, 2017. "Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands," Applied Energy, Elsevier, vol. 190(C), pages 222-231.
    15. Jonghoon Ahn, 2020. "Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season," Sustainability, MDPI, vol. 12(22), pages 1-14, November.
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    1. Jonghoon Ahn, 2022. "A Network-Based Strategy to Increase the Sustainability of Building Supply Air Systems Responding to Unexpected Temperature Patterns," Sustainability, MDPI, vol. 14(22), pages 1-13, November.

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