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Development of an integrated energy benchmark for a multi-family housing complex using district heating

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  • Jeong, Jaewook
  • Hong, Taehoon
  • Ji, Changyoon
  • Kim, Jimin
  • Lee, Minhyun
  • Jeong, Kwangbok

Abstract

The reliable benchmarks are required to evaluate building energy efficiency fairly. This study aims to develop the energy benchmarks and relevant process for a multi-family housing complex (MFHC), which is responsible for huge CO2 emissions in South Korea. A database, including the information on building attributes and energy consumption of 503 MFHCs, was established. The database was classified into three groups based on average enclosed area per household (AEA) through data mining techniques. The benchmarks of site energy use intensity (EUI), source EUI, and CO2 emission intensity (CEI) were developed from Groups 1, 2, and 3. Representatively, the developed benchmarks of CEI for Groups 1, 2, and 3 were 28.17, 24.16, and 20.96kg-CO2/m2y, respectively. A comparative analysis using the operational rating identified that the developed benchmarks could solve the irrationality of the original benchmarks from overall database. In the case of the original benchmarks, 93% of small-AEA-groups and 16% of large-AEA-groups received lower grades. In the case of the developed benchmark, the upper and lower grades in Groups 1–3 were both adjusted to 50%. The proposed process for developing energy benchmark is applicable to evaluate the energy efficiency of other buildings, in other regions.

Suggested Citation

  • Jeong, Jaewook & Hong, Taehoon & Ji, Changyoon & Kim, Jimin & Lee, Minhyun & Jeong, Kwangbok, 2016. "Development of an integrated energy benchmark for a multi-family housing complex using district heating," Applied Energy, Elsevier, vol. 179(C), pages 1048-1061.
  • Handle: RePEc:eee:appene:v:179:y:2016:i:c:p:1048-1061
    DOI: 10.1016/j.apenergy.2016.07.086
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    References listed on IDEAS

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    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    3. Koo, Choongwan & Kim, Hyunjoong & Hong, Taehoon, 2014. "Framework for the analysis of the low-carbon scenario 2020 to achieve the national carbon Emissions reduction target: Focused on educational facilities," Energy Policy, Elsevier, vol. 73(C), pages 356-367.
    4. Muller, Wolfgang & Wiederhold, Eckhard, 2002. "Applying decision tree methodology for rules extraction under cognitive constraints," European Journal of Operational Research, Elsevier, vol. 136(2), pages 282-289, January.
    5. Al-Garni, Ahmed Z. & Zubair, Syed M. & Nizami, Javeed S., 1994. "A regression model for electric-energy-consumption forecasting in Eastern Saudi Arabia," Energy, Elsevier, vol. 19(10), pages 1043-1049.
    6. Koo, Choongwan & Park, Sungki & Hong, Taehoon & Park, Hyo Seon, 2014. "An estimation model for the heating and cooling demand of a residential building with a different envelope design using the finite element method," Applied Energy, Elsevier, vol. 115(C), pages 205-215.
    7. Koo, Choongwan & Hong, Taehoon, 2015. "Development of a dynamic operational rating system in energy performance certificates for existing buildings: Geostatistical approach and data-mining technique," Applied Energy, Elsevier, vol. 154(C), pages 254-270.
    8. Koo, Choongwan & Hong, Taehoon & Lee, Minhyun & Seon Park, Hyo, 2014. "Development of a new energy efficiency rating system for existing residential buildings," Energy Policy, Elsevier, vol. 68(C), pages 218-231.
    9. Hong, Taehoon & Koo, Choongwan & Jeong, Kwangbok, 2012. "A decision support model for reducing electric energy consumption in elementary school facilities," Applied Energy, Elsevier, vol. 95(C), pages 253-266.
    10. Hong, Taehoon & Koo, Choongwan & Kim, Daeho & Lee, Minhyun & Kim, Jimin, 2015. "An estimation methodology for the dynamic operational rating of a new residential building using the advanced case-based reasoning and stochastic approaches," Applied Energy, Elsevier, vol. 150(C), pages 308-322.
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    1. Jeong, Kwangbok & Hong, Taehoon & Kim, Jimin & Lee, Jaewook, 2021. "A data-driven approach for establishing a CO2 emission benchmark for a multi-family housing complex using data mining techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    2. Kang, Hyuna & An, Jongbaek & Kim, Hakpyeong & Ji, Changyoon & Hong, Taehoon & Lee, Seunghye, 2021. "Changes in energy consumption according to building use type under COVID-19 pandemic in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    3. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
    4. Jeong, Jaewook & Hong, Taehoon & Ji, Changyoon & Kim, Jimin & Lee, Minhyun & Jeong, Kwangbok & Koo, Choongwan, 2017. "Improvements of the operational rating system for existing residential buildings," Applied Energy, Elsevier, vol. 193(C), pages 112-124.
    5. Ji, Changyoon & Hong, Taehoon & Kim, Hakpyeong, 2022. "Statistical analysis of greenhouse gas emissions of South Korean residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    6. Zhou, Yuren & Lork, Clement & Li, Wen-Tai & Yuen, Chau & Keow, Yeong Ming, 2019. "Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Cai, Wei & Liu, Fei & Zhang, Hua & Liu, Peiji & Tuo, Junbo, 2017. "Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement," Applied Energy, Elsevier, vol. 202(C), pages 715-725.
    8. Cai, Wei & Wang, Lianguo & Li, Li & Xie, Jun & Jia, Shun & Zhang, Xugang & Jiang, Zhigang & Lai, Kee-hung, 2022. "A review on methods of energy performance improvement towards sustainable manufacturing from perspectives of energy monitoring, evaluation, optimization and benchmarking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    9. Liu, Jiangyan & Chen, Huanxin & Liu, Jiahui & Li, Zhengfei & Huang, Ronggeng & Xing, Lu & Wang, Jiangyu & Li, Guannan, 2017. "An energy performance evaluation methodology for individual office building with dynamic energy benchmarks using limited information," Applied Energy, Elsevier, vol. 206(C), pages 193-205.

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