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Energy assessment method towards low-carbon energy schools

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  • Lizana, Jesus
  • Serrano-Jimenez, Antonio
  • Ortiz, Carlos
  • Becerra, Jose A.
  • Chacartegui, Ricardo

Abstract

Within the building sector, schools have a major social responsibility because of their educational purpose. With the aim of providing a novel energy modelling process to evaluate the real energy performance of school buildings and potential energy savings, minimizing input data collection, this paper presents a new energy assessment method to support the decision-making process towards low-carbon energy schools. The novelty of this method is based on the integration of a coherent set of assumptions and procedures with respect to boundary conditions of schools, derived from their modular basis, common building configuration and space uses, and the model's iterative calibration procedure, based on real building performance, which achieves a high final accuracy. With a reduced set of inputs, hourly dynamic simulations can be performed. The method, integrated in the SchoolEnergy-ACT tool, was tested in two pilot schools. The results show that the method properly fits to schools' energy consumption profiles with high accuracy levels through calibration processes using energy bills, ensuring its feasibility for simulating the energy performance of schools, enabling comparison with other buildings, and informing end-users of potential energy savings. The accuracy of the model can be improved with an iterative, self-learning procedure and detailed energy data.

Suggested Citation

  • Lizana, Jesus & Serrano-Jimenez, Antonio & Ortiz, Carlos & Becerra, Jose A. & Chacartegui, Ricardo, 2018. "Energy assessment method towards low-carbon energy schools," Energy, Elsevier, vol. 159(C), pages 310-326.
    • Handle: RePEc:eee:energy:v:159:y:2018:i:c:p:310-326
    DOI: 10.1016/j.energy.2018.06.147
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    4. 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.
    5. Carmen María Calama-González & Ángel Luis León-Rodríguez & Rafael Suárez, 2019. "Indoor Air Quality Assessment: Comparison of Ventilation Scenarios for Retrofitting Classrooms in a Hot Climate," Energies, MDPI, vol. 12(24), pages 1-20, December.
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    8. 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).
    9. Hettinga, Sanne & van ’t Veer, Rein & Boter, Jaap, 2023. "Large scale energy labelling with models: The EU TABULA model versus machine learning with open data," Energy, Elsevier, vol. 264(C).
    10. Lizana, Jesus & Halloran, Claire E. & Wheeler, Scot & Amghar, Nabil & Renaldi, Renaldi & Killendahl, Markus & Perez-Maqueda, Luis A. & McCulloch, Malcolm & Chacartegui, Ricardo, 2023. "A national data-based energy modelling to identify optimal heat storage capacity to support heating electrification," Energy, Elsevier, vol. 262(PA).
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