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Development of a dynamic operational rating system in energy performance certificates for existing buildings: Geostatistical approach and data-mining technique

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  • Koo, Choongwan
  • Hong, Taehoon

Abstract

The operational rating system in building energy performance certificates (EPCs) has been used for systematically monitoring and diagnosing the energy performance in the operation and maintenance phases of existing buildings. However, there are several limitations of the conventional operational rating system, which can be subdivided into three aspects: (i) building category; (ii) region category; and (iii) space unit size. To overcome these challenges, this study conducted the problem analysis of the conventional operational rating system for existing buildings by using the statistical and geostatistical approaches. Based on the problem analysis, this study developed the dynamic operational rating (DOR) system for existing buildings by using the data-mining technique and the probability approach. The developed DOR system can be used as a tool for building energy performance diagnostics. To validate the applicability of the developed DOR system, educational facilities were selected as the representative type of existing buildings in South Korea. As a result, it was determined that the developed DOR system can solve the irrationality of the conventional operational rating system (i.e., the negative correlation between the space unit size and the CO2 emission density). Namely, the operational ratings of small buildings were adjusted upward while those of large buildings were adjusted downward. The developed DOR system can allow policymakers to establish the reasonable operational rating system for existing buildings, which can motivate the public to actively participate in energy-saving campaigns.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:154:y:2015:i:c:p:254-270
    DOI: 10.1016/j.apenergy.2015.05.003
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    References listed on IDEAS

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    Cited by:

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    3. 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).
    4. Koo, Choongwan & Hong, Taehoon & Jeong, Kwangbok & Ban, Cheolwoo & Oh, Jeongyoon, 2017. "Development of the smart photovoltaic system blind and its impact on net-zero energy solar buildings using technical-economic-political analyses," Energy, Elsevier, vol. 124(C), pages 382-396.
    5. Khayatian, Fazel & Sarto, Luca & Dall'O', Giuliano, 2017. "Building energy retrofit index for policy making and decision support at regional and national scales," Applied Energy, Elsevier, vol. 206(C), pages 1062-1075.
    6. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network," Applied Energy, Elsevier, vol. 306(PA).
    7. Liu, Jiangyan & Wang, Jiangyu & Li, Guannan & Chen, Huanxin & Shen, Limei & Xing, Lu, 2017. "Evaluation of the energy performance of variable refrigerant flow systems using dynamic energy benchmarks based on data mining techniques," Applied Energy, Elsevier, vol. 208(C), pages 522-539.
    8. Park, Hyo Seon & Lee, Minhyun & Kang, Hyuna & Hong, Taehoon & Jeong, Jaewook, 2016. "Development of a new energy benchmark for improving the operational rating system of office buildings using various data-mining techniques," Applied Energy, Elsevier, vol. 173(C), pages 225-237.
    9. Marzouk, Mohamed & Seleem, Noreihan, 2018. "Assessment of existing buildings performance using system dynamics technique," Applied Energy, Elsevier, vol. 211(C), pages 1308-1323.
    10. Vaisi, Salah & Varmazyari, Pouya & Esfandiari, Masoud & Sharbaf, Sara A., 2023. "Developing a multi-level energy benchmarking and certification system for office buildings in a cold climate region," Applied Energy, Elsevier, vol. 336(C).
    11. 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.
    12. Li, Guannan & Hu, Yunpeng & Chen, Huanxin & Li, Haorong & Hu, Min & Guo, Yabin & Liu, Jiangyan & Sun, Shaobo & Sun, Miao, 2017. "Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions," Applied Energy, Elsevier, vol. 185(P1), pages 846-861.
    13. Ma, Jun & Cheng, Jack C.P., 2016. "Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology," Applied Energy, Elsevier, vol. 183(C), pages 182-192.
    14. Aleksandar S. Anđelković & Miroslav Kljajić & Dušan Macura & Vladimir Munćan & Igor Mujan & Mladen Tomić & Željko Vlaović & Borivoj Stepanov, 2021. "Building Energy Performance Certificate—A Relevant Indicator of Actual Energy Consumption and Savings?," Energies, MDPI, vol. 14(12), pages 1-19, June.
    15. 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.
    16. 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.
    17. Shad, Rouzbeh & Khorrami, Mohammad & Ghaemi, Marjan, 2017. "Developing an Iranian green building assessment tool using decision making methods and geographical information system: Case study in Mashhad city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 324-340.
    18. 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.
    19. 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.

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