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Comparative Analysis of Energy Use and Human Comfort by an Intelligent Control Model at the Change of Season

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  • Sung Hoon Yoon

    (Department of Architecture, Cheongju University, Cheongju 28503, Korea)

  • Jonghoon Ahn

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

Abstract

For improving control methods in the thermal environment, various algorithms have been studied to satisfy the specific conditions required by the characteristics of building spaces and to reduce the energy consumed in operation. In this research, a network-based learning control equipped with an adaptive controller is proposed to investigate the control performance for supply air conditions with maintaining the levels of indoor thermal comfort. In order to examine its performance, the proposed model is compared to two different models in terms of the patterns of heating and cooling energy use and the characteristics of operational signals and overshoots. As a result, the energy efficiency of the proposed control has been slightly decreased due to the energy consumption increased by precise controls, but the thermal comfort has improved by about 10.7% more than a conventional thermostat and by about 19.8% more than a deterministic control, respectively. This result can contribute to the reduction of actual installation and maintenance costs by reducing the operating time of dampers and the energy use of heating coils without compromising indoor thermal comfort.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6023-:d:446985
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    References listed on IDEAS

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    1. Hossein Bakhtiari & Jan Akander & Mathias Cehlin & Abolfazl Hayati, 2020. "On the Performance of Night Ventilation in a Historic Office Building in Nordic Climate," Energies, MDPI, vol. 13(16), pages 1-26, August.
    2. 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.
    3. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    4. 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.
    5. Lexuan Zhong & Jing Yuan & Brian Fleck, 2019. "Indoor Environmental Quality Evaluation of Lecture Classrooms in an Institutional Building in a Cold Climate," Sustainability, MDPI, vol. 11(23), pages 1-16, November.
    6. Tuule Mall Kull & Martin Thalfeldt & Jarek Kurnitski, 2020. "PI Parameter Influence on Underfloor Heating Energy Consumption and Setpoint Tracking in nZEBs," Energies, MDPI, vol. 13(8), pages 1-20, April.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Lee-Yong Sung & Jonghoon Ahn, 2020. "Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment," Energies, MDPI, vol. 13(5), pages 1-15, March.
    12. 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.
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    Cited by:

    1. 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.
    2. Zedong Jiao & Xiuli Du & Zhansheng Liu & Liang Liu & Zhe Sun & Guoliang Shi & Ruirui Liu, 2023. "A Review of Theory and Application Development of Intelligent Operation Methods for Large Public Buildings," Sustainability, MDPI, vol. 15(12), pages 1-28, June.

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