IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i21p8974-d436566.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/21/8974/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/21/8974/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hee, W.J. & Alghoul, M.A. & Bakhtyar, B. & Elayeb, OmKalthum & Shameri, M.A. & Alrubaih, M.S. & Sopian, K., 2015. "The role of window glazing on daylighting and energy saving in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 323-343.
    2. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    3. Fang, Tingting & Lahdelma, Risto, 2016. "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system," Applied Energy, Elsevier, vol. 179(C), pages 544-552.
    4. Jeongyoon Oh & Taehoon Hong & Hakpyeong Kim & Jongbaek An & Kwangbok Jeong & Choongwan Koo, 2017. "Advanced Strategies for Net-Zero Energy Building: Focused on the Early Phase and Usage Phase of a Building’s Life Cycle," Sustainability, MDPI, vol. 9(12), pages 1-52, December.
    5. Wang, Yang & Zhao, Fu-Yun & Kuckelkorn, Jens & Spliethoff, Hartmut & Rank, Ernst, 2014. "School building energy performance and classroom air environment implemented with the heat recovery heat pump and displacement ventilation system," Applied Energy, Elsevier, vol. 114(C), pages 58-68.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ghazi Falah & Michael Hoy & Rakhal Sarker, 2000. "Co-existence in Selected Mixed Arab-Jewish Cities in Israel: By Choice or by Default?," Urban Studies, Urban Studies Journal Limited, vol. 37(4), pages 775-796, April.
    2. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    3. Cowell, Frank & Flachaire, Emmanuel & Bandyopadhyay, Sanghamitra, 2009. "Goodness-of-fit: an economic approach," LSE Research Online Documents on Economics 25433, London School of Economics and Political Science, LSE Library.
    4. Aunedi, Marko & Pantaleo, Antonio Marco & Kuriyan, Kamal & Strbac, Goran & Shah, Nilay, 2020. "Modelling of national and local interactions between heat and electricity networks in low-carbon energy systems," Applied Energy, Elsevier, vol. 276(C).
    5. Karel Struhala & Miroslav Čekon & Richard Slávik, 2018. "Life Cycle Assessment of Solar Façade Concepts Based on Transparent Insulation Materials," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    6. Li, Jiarong & Li, Xiangdong & Wang, Yong & Tu, Jiyuan, 2020. "A theoretical model of natural circulation flow and heat transfer within horizontal evacuated tube considering the secondary flow," Renewable Energy, Elsevier, vol. 147(P1), pages 630-638.
    7. Liu, Changyu & Wu, Yangyang & Bian, Ji & Li, Dong & Liu, Xiaoyan, 2018. "Influence of PCM design parameters on thermal and optical performance of multi-layer glazed roof," Applied Energy, Elsevier, vol. 212(C), pages 151-161.
    8. Michaux, Ghislain & Greffet, Rémy & Salagnac, Patrick & Ridoret, Jean-Baptiste, 2019. "Modelling of an airflow window and numerical investigation of its thermal performances by comparison to conventional double and triple-glazed windows," Applied Energy, Elsevier, vol. 242(C), pages 27-45.
    9. Karolis Banionis & Jurga Kumžienė & Arūnas Burlingis & Juozas Ramanauskas & Valdas Paukštys, 2021. "The Changes in Thermal Transmittance of Window Insulating Glass Units Depending on Outdoor Temperatures in Cold Climate Countries," Energies, MDPI, vol. 14(6), pages 1-22, March.
    10. Magnus Dahl & Adam Brun & Oliver S. Kirsebom & Gorm B. Andresen, 2018. "Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data," Energies, MDPI, vol. 11(7), pages 1-16, June.
    11. Burkey, Mark L. & Obeng, Kofi, 2005. "Crash Risk Reduction at Signalized Intersections Using Longitudinal Data," MPRA Paper 36281, University Library of Munich, Germany.
    12. Daniel Griffith & David Wong, 2007. "Modeling population density across major US cities: a polycentric spatial regression approach," Journal of Geographical Systems, Springer, vol. 9(1), pages 53-75, April.
    13. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    14. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
    15. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    16. Gong, Mingju & Zhao, Yin & Sun, Jiawang & Han, Cuitian & Sun, Guannan & Yan, Bo, 2022. "Load forecasting of district heating system based on Informer," Energy, Elsevier, vol. 253(C).
    17. Seok-Hyun Kim & Hakgeun Jeong & Soo Cho, 2019. "A Study on Changes of Window Thermal Performance by Analysis of Physical Test Results in Korea," Energies, MDPI, vol. 12(20), pages 1-17, October.
    18. Rafael Herrera-Limones & Ángel Luis León-Rodríguez & Álvaro López-Escamilla, 2019. "Solar Decathlon Latin America and Caribbean: Comfort and the Balance between Passive and Active Design," Sustainability, MDPI, vol. 11(13), pages 1-17, June.
    19. Pennings, Joost M.E. & Garcia, Philip & Irwin, Scott H., 2011. "Accounting for Heterogeneity in Hedging Behavior: Comparing & Evaluating Grouping Methods," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114787, European Association of Agricultural Economists.
    20. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8974-:d:436566. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.