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Developing two benchmark models for nearly zero energy schools

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  • Attia, Shady
  • Shadmanfar, Niloufar
  • Ricci, Federico

Abstract

The aim of this study is to develop an energy performance data set and two building performance simulation benchmark models for high performance schools in Belgium. The study reports the results of an inventory and field survey conducted on nearly Zero Energy Schools (nZES) and Passive House Schools (PHS) constructed after year 2013. An analysis of energy consumption (electricity and natural gas) and a walkthrough survey were conducted during May 2018. The energy consumption analysis was done for the occupancy period of 2015–2018 based on monthly consumption data. Two building performance simulation models are created in EnergyPlus to benchmark the average energy consumption and building characteristics. The validity of the estimate has been further checked against the public statistics and verified through model calibration and utility bill comparison. The paper provides a timely opportunity to evaluate the real performance of nZES, in relation to design assumptions and how schools’ professionals can turn the energy performance gap challenge to their advantage. The findings on energy needs and use intensity are useful in temperate and continental climates.

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  • Attia, Shady & Shadmanfar, Niloufar & Ricci, Federico, 2020. "Developing two benchmark models for nearly zero energy schools," Applied Energy, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:appene:v:263:y:2020:i:c:s0306261920301264
    DOI: 10.1016/j.apenergy.2020.114614
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    1. 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.
    2. Dimoudi, A. & Kostarela, P., 2009. "Energy monitoring and conservation potential in school buildings in the C′ climatic zone of Greece," Renewable Energy, Elsevier, vol. 34(1), pages 289-296.
    3. Corgnati, Stefano Paolo & Fabrizio, Enrico & Filippi, Marco & Monetti, Valentina, 2013. "Reference buildings for cost optimal analysis: Method of definition and application," Applied Energy, Elsevier, vol. 102(C), pages 983-993.
    4. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    5. Brandão de Vasconcelos, Ana & Pinheiro, Manuel Duarte & Manso, Armando & Cabaço, António, 2015. "A Portuguese approach to define reference buildings for cost-optimal methodologies," Applied Energy, Elsevier, vol. 140(C), pages 316-328.
    6. Gil-Baez, Maite & Padura, Ángela Barrios & Huelva, Marta Molina, 2019. "Passive actions in the building envelope to enhance sustainability of schools in a Mediterranean climate," Energy, Elsevier, vol. 167(C), pages 144-158.
    7. 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.
    8. Ballarini, Ilaria & Corgnati, Stefano Paolo & Corrado, Vincenzo, 2014. "Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project," Energy Policy, Elsevier, vol. 68(C), pages 273-284.
    9. Liang, Han-Hsi & Lin, Tzu-Ping & Hwang, Ruey-Lung, 2012. "Linking occupants’ thermal perception and building thermal performance in naturally ventilated school buildings," Applied Energy, Elsevier, vol. 94(C), pages 355-363.
    10. 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.
    11. Zomorodian, Zahra Sadat & Tahsildoost, Mohammad & Hafezi, Mohammadreza, 2016. "Thermal comfort in educational buildings: A review article," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 895-906.
    12. Niemelä, Tuomo & Kosonen, Risto & Jokisalo, Juha, 2016. "Cost-optimal energy performance renovation measures of educational buildings in cold climate," Applied Energy, Elsevier, vol. 183(C), pages 1005-1020.
    13. Zeiler, Wim & Boxem, Gert, 2013. "Net-zero energy building schools," Renewable Energy, Elsevier, vol. 49(C), pages 282-286.
    14. Georgios Martinopoulos & Vasiliki Kikidou & Dimitrios Bozis, 2018. "Energy Assessment of Building Physics Principles in Secondary Education Buildings," Energies, MDPI, vol. 11(11), pages 1-15, October.
    15. Chung, William & Hui, Y.V. & Lam, Y. Miu, 2006. "Benchmarking the energy efficiency of commercial buildings," Applied Energy, Elsevier, vol. 83(1), pages 1-14, January.
    16. 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.
    17. Luo, Xuan & Hong, Tianzhen & Chen, Yixing & Piette, Mary Ann, 2017. "Electric load shape benchmarking for small- and medium-sized commercial buildings," Applied Energy, Elsevier, vol. 204(C), pages 715-725.
    18. Attia, Shady & Evrard, Arnaud & Gratia, Elisabeth, 2012. "Development of benchmark models for the Egyptian residential buildings sector," Applied Energy, Elsevier, vol. 94(C), pages 270-284.
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    Cited by:

    1. Shady Attia, 2020. "Spatial and Behavioral Thermal Adaptation in Net Zero Energy Buildings: An Exploratory Investigation," Sustainability, MDPI, vol. 12(19), pages 1-15, September.
    2. Pengying Wang & Shuo Zhang, 2022. "Retrofitting Strategies Based on Orthogonal Array Testing to Develop Nearly Zero Energy Buildings," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    3. 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).
    4. Hye-Jin Kim & Do-Young Choi & Donghyun Seo, 2021. "Development and Verification of Prototypical Office Buildings Models Using the National Building Energy Consumption Survey in Korea," Sustainability, MDPI, vol. 13(7), pages 1-15, March.
    5. Attia, Shady & Canonge, Théophile & Popineau, Mathieu & Cuchet, Mathilde, 2022. "Developing a benchmark model for renovated, nearly zero-energy, terraced dwellings," Applied Energy, Elsevier, vol. 306(PB).
    6. Yizhe Xu & Chengchu Yan & Hao Qian & Liang Sun & Gang Wang & Yanlong Jiang, 2021. "A Novel Optimization Method for Conventional Primary and Secondary School Classrooms in Southern China Considering Energy Demand, Thermal Comfort and Daylighting," Sustainability, MDPI, vol. 13(23), pages 1-19, November.

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