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A New Generation of Thermal Energy Benchmarks for University Buildings

Author

Listed:
  • Salah Vaisi

    (Department of Architecture, Faculty of Art and Architecture, University of Kurdistan (UOK), Sanandaj 0871, Iran)

  • Saleh Mohammadi

    (Department of Architecture, Faculty of Art and Architecture, University of Kurdistan (UOK), Sanandaj 0871, Iran
    Department of Architectural Engineering + Technology, Faculty of Architecture and the Built Environment, Delft University of Technology (TU Delft), 2628BX Delft, The Netherlands)

  • Benedetto Nastasi

    (Department of Planning, Design & Technology of Architecture, Sapienza University of Rome, Via Flaminia 72, 00196 Rome, Italy)

  • Kavan Javanroodi

    (Solar Energy and Building Physics Laboratory (LESO-PB), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland)

Abstract

In 2008, the Chartered Institution of Building Services Engineers (CIBSE TM46 UC) presented an annual-fixed thermal energy benchmark of 240 kWh/m 2 /yr for university campus (UC) buildings as an attempt to reduce energy consumption in public buildings. However, the CIBSE TM46 UC benchmark fails to consider the difference between energy demand in warm and cold months, as the thermal performance of buildings largely depends on the ambient temperature. This paper presents a new generation of monthly thermal energy benchmarks (MTEBs) using two computational methods including mixed-use model and converter model, which consider the variations of thermal demand throughout a year. MTEBs were generated using five basic variables, including mixed activities in the typical college buildings, university campus revised benchmark (UCrb), typical operation of heating systems, activities impact, and heating degree days. The results showed that MTEBs vary from 24 kWh/m 2 /yr in January to one and nearly zero kWh/m 2 /yr in June and July, respectively. Based on the detailed assessments, a typical college building was defined in terms of the percentage of its component activities. Compared with the 100% estimation error of the TM46 UC benchmark, the maximum 21% error of the developed methodologies is a significant achievement. The R-squared value of 99% confirms the reliability of the new generation of benchmarks.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6606-:d:462032
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    References listed on IDEAS

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    1. Arjunan, Pandarasamy & Poolla, Kameshwar & Miller, Clayton, 2020. "EnergyStar++: Towards more accurate and explanatory building energy benchmarking," Applied Energy, Elsevier, vol. 276(C).
    2. Francesco Mancini & Benedetto Nastasi, 2019. "Energy Retrofitting Effects on the Energy Flexibility of Dwellings," Energies, MDPI, vol. 12(14), pages 1-19, July.
    3. Burman, Esfand & Mumovic, Dejan & Kimpian, Judit, 2014. "Towards measurement and verification of energy performance under the framework of the European directive for energy performance of buildings," Energy, Elsevier, vol. 77(C), pages 153-163.
    4. Javanroodi, Kavan & Mahdavinejad, Mohammadjavad & Nik, Vahid M., 2018. "Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate," Applied Energy, Elsevier, vol. 231(C), pages 714-746.
    5. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
    6. Yan, Chengchu & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "A multi-level energy performance diagnosis method for energy information poor buildings," Energy, Elsevier, vol. 83(C), pages 189-203.
    7. Hye Gi Kim & Sun Sook Kim, 2020. "Development of Energy Benchmarks for Office Buildings Using the National Energy Consumption Database," Energies, MDPI, vol. 13(4), pages 1-18, February.
    8. 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.
    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.
    10. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    11. Pasichnyi, Oleksii & Wallin, Jörgen & Levihn, Fabian & Shahrokni, Hossein & Kordas, Olga, 2019. "Energy performance certificates — New opportunities for data-enabled urban energy policy instruments?," Energy Policy, Elsevier, vol. 127(C), pages 486-499.
    12. Park, June Young & Yang, Xiya & Miller, Clayton & Arjunan, Pandarasamy & Nagy, Zoltan, 2019. "Apples or oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset," Applied Energy, Elsevier, vol. 236(C), pages 1280-1295.
    13. AfDB AfDB, . "Annual Report 2012," Annual Report, African Development Bank, number 461.
    14. Roth, Jonathan & Rajagopal, Ram, 2018. "Benchmarking building energy efficiency using quantile regression," Energy, Elsevier, vol. 152(C), pages 866-876.
    15. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
    16. Kristaps Locmelis & Dagnija Blumberga & Andra Blumberga & Anna Kubule, 2020. "Benchmarking of Industrial Energy Efficiency. Outcomes of an Energy Audit Policy Program," Energies, MDPI, vol. 13(9), pages 1-15, May.
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    Cited by:

    1. Benedetto Nastasi & Francesco Mancini, 2021. "Procedures and Methodologies for the Control and Improvement of Energy-Environmental Quality in Construction," Energies, MDPI, vol. 14(9), pages 1-2, April.
    2. 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).
    3. Salah Vaisi & Saleh Mohammadi & Kyoumars Habibi, 2021. "Heat Mapping, a Method for Enhancing the Sustainability of the Smart District Heat Networks," Energies, MDPI, vol. 14(17), pages 1-17, September.

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