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Prediction for Overheating Risk Based on Deep Learning in a Zero Energy Building

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  • Yue Yuan

    (Department of Civil and Environmental System Engineering, Graduate school of Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Korea)

  • Jisoo Shim

    (Department of Civil and Environmental System Engineering, Graduate school of Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Korea)

  • Seungkeon Lee

    (Department of Civil and Environmental System Engineering, Graduate school of Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Korea)

  • Doosam Song

    (School of Civil, Architectural Engineering, and Landscape Architecture, Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Korea)

  • Joowook Kim

    (Center for Building Environment, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea)

Abstract

The Passive House standard has become the standard for many countries in the construction of the Zero Energy Building (ZEB). Korea also adopted the standard and has achieved great success in building energy savings. However, some issues remain with ZEBs in Korea. Among them, this study aims to discuss overheating issues. Field measurements were carried out to analyze the overheating risk for a library built as a ZEB. A data-driven overheating risk prediction model was developed to analyze the overheating risk, requiring only a small amount of data and extending the analysis throughout the year. The main factors causing overheating during both the cooling season and the intermediate seasons are also analyzed in detail. The overheating frequency exceeded 60% of days in July and August, the midsummer season in Korea. Overheating also occurred during the intermediate seasons when air conditioners were off, such as in May and October in Korea. Overheating during the cooling season was caused mainly by unexpected increases in occupancy rate, while overheating in the mid-term was mainly due to an increase in solar irradiation. This is because domestic ZEB standards define the reinforcement of insulation and airtight performance, but there are no standards for solar insolation through windows or for internal heat generation. The results of this study suggest that a fixed performance standard for ZEBs that does not reflect the climate or cultural characteristics of the region in which a ZEB is built may not result in energy savings at the operational stage and may not guarantee the thermal comfort of occupants.

Suggested Citation

  • Yue Yuan & Jisoo Shim & Seungkeon Lee & Doosam Song & Joowook Kim, 2020. "Prediction for Overheating Risk Based on Deep Learning in a Zero Energy Building," Sustainability, MDPI, vol. 12(21), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8974-:d:436566
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    References listed on IDEAS

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    Cited by:

    1. Kosara Kujundzic & Slavica Stamatovic Vuckovic & Ana Radivojević, 2023. "Toward Regenerative Sustainability: A Passive Design Comfort Assessment Method of Indoor Environment," Sustainability, MDPI, vol. 15(1), pages 1-33, January.
    2. Denisa Valachova & Andrea Badurova & Iveta Skotnicova, 2021. "Thermal Technical Analysis of Lightweight Timber-Based External Wall Structures with Ventilated Air Gap," Sustainability, MDPI, vol. 13(1), pages 1-15, January.
    3. Zhang, Wuxia & Wu, Yupeng & Calautit, John Kaiser, 2022. "A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

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