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A Novel Optimization Method for Conventional Primary and Secondary School Classrooms in Southern China Considering Energy Demand, Thermal Comfort and Daylighting

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  • Yizhe Xu

    (Key Laboratory of Aircraft Environment Control and Life Support, MIIT, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China)

  • Chengchu Yan

    (College of Urban Construction, Nanjing Tech University, No. 200 North Zhongshan Road, Nanjing 210009, China)

  • Hao Qian

    (Qinhuai Education Financial Assets Management Center, Nanjing 210002, China)

  • Liang Sun

    (Civil Aviation Management Institute of China, No. 3 Huajiadi East Road, Chaoyang District, Beijing 100102, China)

  • Gang Wang

    (Nanjing Zhongyi Architectural Design Institute Co., Ltd., Nanjing 210009, China)

  • Yanlong Jiang

    (Key Laboratory of Aircraft Environment Control and Life Support, MIIT, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China)

Abstract

The classroom environment is of great significance for the health of primary and secondary school students, but a comfortable indoor environment often requires higher energy consumption. This paper presents a multi-objective optimization method based on an artificial neural network (ANN) model, which can help designers efficiently optimize the design of primary and secondary school classrooms in southern China. In this optimization method, first, the optimization objectives and variables are determined according to building characteristics, and the physical model is established through simulation software (EnergyPlus) to generate the sample space. Second, sensitivity analysis is carried out for each optimization variable, and the physical model is modified according to the results to regenerate the sample space. Third, the ANN model is trained by using the regenerated sample space, and the Pareto optimal solution is generated through the use of the non-dominated sorting genetic algorithm II (NSGA-II). Finally, the effectiveness of the multi-objective optimization method is proven through a typical case of primary and secondary school classrooms in Nanjing, China. The results show that, compared with the benchmark scheme, TES decreased by 810.8 kWh at most, PT increased by 47.8% at most and DI increased by 4.2% at most.

Suggested Citation

  • Yizhe Xu & Chengchu Yan & Hao Qian & Liang Sun & Gang Wang & Yanlong Jiang, 2021. "A Novel Optimization Method for Conventional Primary and Secondary School Classrooms in Southern China Considering Energy Demand, Thermal Comfort and Daylighting," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13119-:d:688892
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    1. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    2. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
    3. Pilechiha, Peiman & Mahdavinejad, Mohammadjavad & Pour Rahimian, Farzad & Carnemolla, Phillippa & Seyedzadeh, Saleh, 2020. "Multi-objective optimisation framework for designing office windows: quality of view, daylight and energy efficiency," Applied Energy, Elsevier, vol. 261(C).
    4. Attia, Shady & Shadmanfar, Niloufar & Ricci, Federico, 2020. "Developing two benchmark models for nearly zero energy schools," Applied Energy, Elsevier, vol. 263(C).
    5. Flannery, Mark J. & Rangan, Kasturi P., 2006. "Partial adjustment toward target capital structures," Journal of Financial Economics, Elsevier, vol. 79(3), pages 469-506, March.
    6. Chen, Jianli & Gao, Xinghua & Hu, Yuqing & Zeng, Zhaoyun & Liu, Yanan, 2019. "A meta-model-based optimization approach for fast and reliable calibration of building energy models," Energy, Elsevier, vol. 188(C).
    7. A.M. Fogheri, 2015. "Energy Efficiency in Public Buildings," Rivista economica del Mezzogiorno, Società editrice il Mulino, issue 3-4, pages 763-784.
    8. Chen, Xi & Yang, Hongxing & Zhang, Weilong, 2018. "Simulation-based approach to optimize passively designed buildings: A case study on a typical architectural form in hot and humid climates," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P2), pages 1712-1725.
    9. Zomorodian, Zahra Sadat & Tahsildoost, Mohammad & Hafezi, Mohammadreza, 2016. "Thermal comfort in educational buildings: A review article," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 895-906.
    10. Yan, Chengchu & Gang, Wenjie & Niu, Xiaofeng & Peng, Xujian & Wang, Shengwei, 2017. "Quantitative evaluation of the impact of building load characteristics on energy performance of district cooling systems," Applied Energy, Elsevier, vol. 205(C), pages 635-643.
    11. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
    12. Zhai, Yingni & Wang, Yi & Huang, Yanqiu & Meng, Xiaojing, 2019. "A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance," Renewable Energy, Elsevier, vol. 134(C), pages 1190-1199.
    13. Ciardiello, Adriana & Rosso, Federica & Dell'Olmo, Jacopo & Ciancio, Virgilio & Ferrero, Marco & Salata, Ferdinando, 2020. "Multi-objective approach to the optimization of shape and envelope in building energy design," Applied Energy, Elsevier, vol. 280(C).
    14. Ascione, Fabrizio & Bianco, Nicola & Maria Mauro, Gerardo & Napolitano, Davide Ferdinando, 2019. "Building envelope design: Multi-objective optimization to minimize energy consumption, global cost and thermal discomfort. Application to different Italian climatic zones," Energy, Elsevier, vol. 174(C), pages 359-374.
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