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Research on a Visual Comfort Model Based on Individual Preference in China through Machine Learning Algorithm

Author

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  • Guofeng Ma

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Xuhui Pan

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

Abstract

Recently, decreasing energy consumption under the premise of building comfort has become a popular topic, especially visual comfort. Existing research on visual comfort lacks a standard of how to select indicators. Moreover, studies on individual visual preference considering the interaction between internal and external environment are few. In this paper, we ranked common visual indicators by the cloud model combined with the failure mode and effect analysis (FMEA) and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS). Unsatisfied vertical illuminance, daylight glare index, luminance ratio, and shadow position are the top four indicators. Based on these indicators, we also built the individual visual comfort model through five categories of personalized data obtained from the experiment, which was trained by four machine learning algorithms. The results show that random forest has the best prediction performance and support vector machine is second. Gaussian mixed model and classification tree have the worst performance of stability and accuracy. In addition, this study also programmed a BIM plug-in integrating environmental data and personal preference data to predict appropriate vertical illuminance for a specific occupant. Thus, managers can adjust the intensity of artificial light in the office by increasing or decreasing the height of table lamps, saving energy and improving occupant comfort. This novel model will serve as a paradigm for selecting visual indicators and make indoor space be tailored to meet individual visual preferences.

Suggested Citation

  • Guofeng Ma & Xuhui Pan, 2021. "Research on a Visual Comfort Model Based on Individual Preference in China through Machine Learning Algorithm," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7602-:d:590103
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    References listed on IDEAS

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    1. Hossein Safari & Zahra Faraji & Setareh Majidian, 2016. "Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 475-486, April.
    2. Carlucci, Salvatore & Causone, Francesco & De Rosa, Francesco & Pagliano, Lorenzo, 2015. "A review of indices for assessing visual comfort with a view to their use in optimization processes to support building integrated design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 1016-1033.
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    Cited by:

    1. Bigerna, Simona & D'Errico, Maria Chiara & Polinori, Paolo, 2022. "Understanding the green-growth: which pathways cities undertake in their climate programs," MPRA Paper 114156, University Library of Munich, Germany.
    2. Xiaoming Yang & Shamsulariffin Samsudin & Yuxuan Wang & Yubin Yuan & Tengku Fadilah Tengku Kamalden & Sam Shor Nahar bin Yaakob, 2023. "Application of Target Detection Method Based on Convolutional Neural Network in Sustainable Outdoor Education," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    3. Jiao Xue & Yige Fan & Zhanxun Dong & Xiao Hu & Jiatong Yue, 2022. "Improving Visual Comfort and Health through the Design of a Local Shading Device," IJERPH, MDPI, vol. 19(7), pages 1-20, April.
    4. Mohammed Lami & Faris Al-naemi & Hameed Alrashidi & Walid Issa, 2022. "Quantifying of Vision through Polymer Dispersed Liquid Crystal Double-Glazed Window," Energies, MDPI, vol. 15(9), pages 1-23, April.
    5. Amir Faraji & Maria Rashidi & Fatemeh Rezaei & Payam Rahnamayiezekavat, 2023. "A Meta-Synthesis Review of Occupant Comfort Assessment in Buildings (2002–2022)," Sustainability, MDPI, vol. 15(5), pages 1-36, February.

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