IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v336y2023ics0306261923001885.html
   My bibliography  Save this article

Developing a multi-level energy benchmarking and certification system for office buildings in a cold climate region

Author

Listed:
  • Vaisi, Salah
  • Varmazyari, Pouya
  • Esfandiari, Masoud
  • Sharbaf, Sara A.

Abstract

Energy benchmarking is an accurate tool to measure, monitor, and reduce end-use energy consumption in the building sector using comparison scenarios. Various studies have applied Bottom-Up energy consumption assessment to compare the energy performance of a group of buildings with a benchmark. The Bottom-Up method mostly relies on simulating an ideal building to develop a benchmark. However, this study has developed a Top-Down energy benchmarking methodology based on the actual energy consumption within a cluster of governmental office buildings. The method presents multi-level benchmarks to provide a detailed policy for improving the energy efficiency of buildings in the short or long-term. In an exploratory study, 26 office buildings in a cold climate region were investigated to identify the multi-level benchmarks. Four general benchmark levels were developed, including the ‘Best Practice, ‘Good Practice’, ‘Benchmark’, and ‘Poor Practice’. In addition, 15 energy efficiency classes were also introduced and applied as a base for developing the ‘Energy Performance Certificate’ method. The results indicate that the benchmark values for electricity and natural gas are 40 and 252 kWh/m2/yr, respectively, while the total energy benchmark is 292 kWh/m2/yr. According to the benchmarking results, 69% of the case studies were inefficient, 23% were labeled ‘C’, and no cases were labeled ‘A’ or ‘B’. The multi-level benchmarking system can provide quick and clear guidance for building designers, operators, and government regulation/enforcement agencies; thus, it can apply at local, regional, and international levels. These benchmark levels establish reference points for measuring and rewarding good performance, on the other hand, it recognizes poor-performance buildings and prioritizes them for energy efficiency improvement. The method can be replicated in various climates and urban scales.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:336:y:2023:i:c:s0306261923001885
    DOI: 10.1016/j.apenergy.2023.120824
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923001885
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.120824?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. ChungYeon Won & SangTae No & Qamar Alhadidi, 2019. "Factors Affecting Energy Performance of Large-Scale Office Buildings: Analysis of Benchmarking Data from New York City and Chicago," Energies, MDPI, vol. 12(24), pages 1-17, December.
    2. Lu, Mengxue & Lai, Joseph, 2020. "Review on carbon emissions of commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    3. Salah Vaisi & Saleh Mohammadi & Benedetto Nastasi & Kavan Javanroodi, 2020. "A New Generation of Thermal Energy Benchmarks for University Buildings," Energies, MDPI, vol. 13(24), pages 1-18, December.
    4. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
    5. Roth, Jonathan & Rajagopal, Ram, 2018. "Benchmarking building energy efficiency using quantile regression," Energy, Elsevier, vol. 152(C), pages 866-876.
    6. Ali Amiri & Juudit Ottelin & Jaana Sorvari, 2019. "Are LEED-Certified Buildings Energy-Efficient in Practice?," Sustainability, MDPI, vol. 11(6), pages 1-14, March.
    7. Li, Ming-Jia & Tao, Wen-Quan, 2017. "Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry," Applied Energy, Elsevier, vol. 187(C), pages 203-215.
    8. Mui, K.W. & Wong, L.T. & Law, L.Y., 2007. "An energy benchmarking model for ventilation systems of air-conditioned offices in subtropical climates," Applied Energy, Elsevier, vol. 84(1), pages 89-98, January.
    9. Arjunan, Pandarasamy & Poolla, Kameshwar & Miller, Clayton, 2020. "EnergyStar++: Towards more accurate and explanatory building energy benchmarking," Applied Energy, Elsevier, vol. 276(C).
    10. 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.
    11. Wang, Ran & Feng, Wei & Wang, Lan & Lu, Shilei, 2021. "A comprehensive evaluation of zero energy buildings in cold regions: Actual performance and key technologies of cases from China, the US, and the European Union," Energy, Elsevier, vol. 215(PA).
    12. 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.
    13. Böhringer, Christoph & Rutherford, Thomos F., 2009. "Integrated assessment of energy policies: Decomposing top-down and bottom-up," Journal of Economic Dynamics and Control, Elsevier, vol. 33(9), pages 1648-1661, September.
    14. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    15. Paola Marrone & Paola Gori & Francesco Asdrubali & Luca Evangelisti & Laura Calcagnini & Gianluca Grazieschi, 2018. "Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting," Energies, MDPI, vol. 11(3), pages 1-20, March.
    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. Li, Tian & Bie, Haipei & Lu, Yi & Sawyer, Azadeh Omidfar & Loftness, Vivian, 2024. "MEBA: AI-powered precise building monthly energy benchmarking approach," Applied Energy, Elsevier, vol. 359(C).

    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. Salah Vaisi & Saleh Mohammadi & Benedetto Nastasi & Kavan Javanroodi, 2020. "A New Generation of Thermal Energy Benchmarks for University Buildings," Energies, MDPI, vol. 13(24), pages 1-18, December.
    2. 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.
    3. Salvatori, Simone & Benedetti, Miriam & Bonfà, Francesca & Introna, Vito & Ubertini, Stefano, 2018. "Inter-sectorial benchmarking of compressed air generation energy performance: Methodology based on real data gathering in large and energy-intensive industrial firms," Applied Energy, Elsevier, vol. 217(C), pages 266-280.
    4. Benedetti, Miriam & Bonfa', Francesca & Bertini, Ilaria & Introna, Vito & Ubertini, Stefano, 2018. "Explorative study on Compressed Air Systems’ energy efficiency in production and use: First steps towards the creation of a benchmarking system for large and energy-intensive industrial firms," Applied Energy, Elsevier, vol. 227(C), pages 436-448.
    5. 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).
    6. Andrews, Abigail & Jain, Rishee K., 2022. "Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking," Applied Energy, Elsevier, vol. 327(C).
    7. Ye, Zhongnan & Cheng, Kuangly & Hsu, Shu-Chien & Wei, Hsi-Hsien & Cheung, Clara Man, 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach," Applied Energy, Elsevier, vol. 301(C).
    8. Marzouk, Mohamed & Seleem, Noreihan, 2018. "Assessment of existing buildings performance using system dynamics technique," Applied Energy, Elsevier, vol. 211(C), pages 1308-1323.
    9. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
    10. Hsu, David, 2015. "Identifying key variables and interactions in statistical models of building energy consumption using regularization," Energy, Elsevier, vol. 83(C), pages 144-155.
    11. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
    12. Guo, Jinyu & Ma, Jinji & Li, Zhengqiang & Hong, Jin, 2022. "Building a top-down method based on machine learning for evaluating energy intensity at a fine scale," Energy, Elsevier, vol. 255(C).
    13. Xujie Sun & Xiaocun Zhang, 2024. "Assessment and Driving Factors of Embodied Carbon Emissions in the Construction Sector: Evidence from 2005 to 2021 in Northeast China," Sustainability, MDPI, vol. 16(13), pages 1-18, July.
    14. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation," Energy, Elsevier, vol. 244(PB).
    15. Moya, Diego & Torres, Roberto & Stegen, Sascha, 2016. "Analysis of the Ecuadorian energy audit practices: A review of energy efficiency promotion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 289-296.
    16. Jaqueline Litardo & Ruben Hidalgo-Leon & Guillermo Soriano, 2021. "Energy Performance and Benchmarking for University Classrooms in Hot and Humid Climates," Energies, MDPI, vol. 14(21), pages 1-17, October.
    17. Theodoridou, Ifigeneia & Papadopoulos, Agis M. & Hegger, Manfred, 2012. "A feasibility evaluation tool for sustainable cities – A case study for Greece," Energy Policy, Elsevier, vol. 44(C), pages 207-216.
    18. Li, Tian & Bie, Haipei & Lu, Yi & Sawyer, Azadeh Omidfar & Loftness, Vivian, 2024. "MEBA: AI-powered precise building monthly energy benchmarking approach," Applied Energy, Elsevier, vol. 359(C).
    19. 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).
    20. Goh, Tian & Ang, B.W., 2020. "Four reasons why there is so much confusion about energy efficiency," Energy Policy, Elsevier, vol. 146(C).

    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:eee:appene:v:336:y:2023:i:c:s0306261923001885. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.