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Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings

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  • Re Cecconi, F.
  • Moretti, N.
  • Tagliabue, L.C.

Abstract

School buildings in Italy are outdated, in critical maintenance conditions and they often perform below acceptable service levels and quality standards. Nevertheless, data supporting renovation policies are missing or very expensive to be obtained. The paper presents a method for evaluating building's energy savings potential, using the Building Energy Certification (Certificazione Energetica degli Edifici - CENED) open database. The aim of the research concerns the development of a data-driven set of methods, based on the use of open data, machine learning (ML) and Geographic Information Systems (GIS) to support regional energy retrofit policies on school buildings. The main advantage concerns the possibility to predict the post-retrofit energy savings, avoiding the expensive on-site Condition Assessment (CA) phase. Data have been first clustered to identify the most common thermo-physical properties of the envelope, then three retrofit scenarios have been defined, to allow the retrofit of homogeneous types of buildings. The energy saving potentials have been evaluated through the implementation of eight Artificial Neural Networks. Ultimately, data have been geolocated and further processed to support the definition of the energy retrofit policies for the most critical regional areas. The Lombardy region has been chosen as case study to test the robustness of the proposed methods. The results of the case study proved that school buildings energy retrofit policies can be supported and defined using available open data, ML and GIS. The future developments of the research concern the further integration of GIS for retrofit cost assessment and scenario analysis.

Suggested Citation

  • Re Cecconi, F. & Moretti, N. & Tagliabue, L.C., 2019. "Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 266-277.
    • Handle: RePEc:eee:rensus:v:110:y:2019:i:c:p:266-277
    DOI: 10.1016/j.rser.2019.04.073
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    References listed on IDEAS

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    Cited by:

    1. Giuseppe Aruta & Fabrizio Ascione & Nicola Bianco & Teresa Iovane & Margherita Mastellone, 2023. "Assessment of the Incentive Rate to Favor the Energy Retrofit of Public Buildings: A Comprehensive Approach for an Italian University Facility," Energies, MDPI, vol. 16(11), pages 1-16, June.
    2. Ma, Dingyuan & Li, Xiaodong & Lin, Borong & Zhu, Yimin, 2023. "An intelligent retrofit decision-making model for building program planning considering tacit knowledge and multiple objectives," Energy, Elsevier, vol. 263(PB).
    3. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    4. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    5. Miroslav Variny, 2021. "Comment on Pietrapertosa et al. How to Prioritize Energy Efficiency Intervention in Municipal Public Buildings to Decrease CO 2 Emissions? A Case Study from Italy. Int. J. Environ. Res. Public Health ," IJERPH, MDPI, vol. 18(8), pages 1-12, April.
    6. Yang, Xiu'e & Liu, Shuli & Zou, Yuliang & Ji, Wenjie & Zhang, Qunli & Ahmed, Abdullahi & Han, Xiaojing & Shen, Yongliang & Zhang, Shaoliang, 2022. "Energy-saving potential prediction models for large-scale building: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. Jenny von Platten & Claes Sandels & Kajsa Jörgensson & Viktor Karlsson & Mikael Mangold & Kristina Mjörnell, 2020. "Using Machine Learning to Enrich Building Databases—Methods for Tailored Energy Retrofits," Energies, MDPI, vol. 13(10), pages 1-22, May.
    9. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).

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