IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipds0306261924020476.html
   My bibliography  Save this article

Energy classification of urban districts to map buildings and prioritize energy retrofit interventions: A novel fast tool

Author

Listed:
  • Aruta, Giuseppe
  • Ascione, Fabrizio
  • Bianco, Nicola
  • Bindi, Luisa
  • Iovane, Teresa

Abstract

This paper introduces a new methodology designed to assess the energy performance of urban districts. By integrating building energy performance standards with automated spreadsheet tools, the study provides a framework for quickly analyzing urban energy certificates to identify key areas for energy efficiency improvements. The model considers several factors influencing thermal and energy performance, such as heat transfer through the building envelope, internal heat gains, and energy supply from HVAC systems during both heating and cooling seasons. By evaluating energy needs for heating, cooling, domestic hot water, lighting, and equipment, the methodology offers a comprehensive view of building energy consumption. The adaptable spreadsheet model generates various outputs based on factors like architectural features, expositions, systems for a total of 30 inputs. The tool, designed to be as simple as possible while still ensuring accurate predictions of the energy needs of the analyzed buildings, serves three main purposes. First, it speeds up the energy modeling process of buildings at various scales. Second, it provides a user-friendly system that anyone can use to calculate a building's energy needs and its corresponding energy class. Finally, it suggests optimization strategies by proposing refurbishment solutions for the targeted building stock. Future development includes incorporating this model into geographic information systems for spatial mapping of urban energy districts, offering useful insights for focused interventions and sustainable urban development.

Suggested Citation

  • Aruta, Giuseppe & Ascione, Fabrizio & Bianco, Nicola & Bindi, Luisa & Iovane, Teresa, 2025. "Energy classification of urban districts to map buildings and prioritize energy retrofit interventions: A novel fast tool," Applied Energy, Elsevier, vol. 377(PD).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pd:s0306261924020476
    DOI: 10.1016/j.apenergy.2024.124664
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924020476
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124664?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. Battini, Federico & Pernigotto, Giovanni & Gasparella, Andrea, 2023. "District-level validation of a shoeboxing simplification algorithm to speed-up Urban Building Energy Modeling simulations," Applied Energy, Elsevier, vol. 349(C).
    2. De Rosa, Mattia & Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2014. "Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach," Applied Energy, Elsevier, vol. 128(C), pages 217-229.
    3. Aruta, Giuseppe & Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria, 2023. "Sustainability and energy communities: Assessing the potential of building energy retrofit and renewables to lead the local energy transition," Energy, Elsevier, vol. 282(C).
    Full references (including those not matched with items on IDEAS)

    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. Omar, M.N. & Samak, A.A. & Keshek, M.H. & Elsisi, S.F., 2020. "Simulation and validation model for using the energy produced from broiler litter waste in their house and its requirement of energy," Renewable Energy, Elsevier, vol. 159(C), pages 920-928.
    2. Liang Chen & Yuanfan Zheng & Jia Yu & Yuanhang Peng & Ruipeng Li & Shilingyun Han, 2024. "A GIS-Based Approach for Urban Building Energy Modeling under Climate Change with High Spatial and Temporal Resolution," Energies, MDPI, vol. 17(17), pages 1-24, August.
    3. Yuzhe Qin & Qing Cheng, 2025. "Optimization Study of Photovoltaic Cell Arrangement Strategies in Greenhouses," Energies, MDPI, vol. 18(1), pages 1-28, January.
    4. Papada, Lefkothea & Kaliampakos, Dimitris, 2016. "Developing the energy profile of mountainous areas," Energy, Elsevier, vol. 107(C), pages 205-214.
    5. Perera, D.W.U. & Winkler, D. & Skeie, N.-O., 2016. "Multi-floor building heating models in MATLAB and Modelica environments," Applied Energy, Elsevier, vol. 171(C), pages 46-57.
    6. Felipe Rosas-Díaz & David Gilberto García-Hernández & Cesar A. Juárez-Alvarado, 2024. "Development of Lignocellulosic-Based Insulation Materials from Agave fourcroydes and Washingtonia filifera for Use in Sustainable Buildings," Sustainability, MDPI, vol. 16(13), pages 1-17, June.
    7. Pilechiha, Peiman & Mahdavinejad, Mohammadjavad & Pour Rahimian, Farzad & Carnemolla, Phillippa & Seyedzadeh, Saleh, 2020. "Multi-objective optimisation framework for designing office windows: quality of view, daylight and energy efficiency," Applied Energy, Elsevier, vol. 261(C).
    8. Yucheng Guo & Jie Shi & Tong Guo & Fei Guo & Feng Lu & Lingqi Su, 2024. "Grey-Box Method for Urban Building Energy Modelling: Advancements and Potentials," Energies, MDPI, vol. 17(21), pages 1-25, October.
    9. Zihao Li & Daniel Friedrich & Gareth P. Harrison, 2020. "Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model," Energies, MDPI, vol. 13(4), pages 1-20, February.
    10. Omarov, Bekarys & Memon, Shazim Ali & Kim, Jong, 2023. "A novel approach to develop climate classification based on degree days and building energy performance," Energy, Elsevier, vol. 267(C).
    11. Aste, Niccolò & Leonforte, Fabrizio & Manfren, Massimiliano & Mazzon, Manlio, 2015. "Thermal inertia and energy efficiency – Parametric simulation assessment on a calibrated case study," Applied Energy, Elsevier, vol. 145(C), pages 111-123.
    12. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Jiang, Tao & Yu, Xiaodan, 2017. "Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system," Applied Energy, Elsevier, vol. 194(C), pages 386-398.
    13. Rosa Francesca De Masi & Gerardo Maria Mauro & Silvia Ruggiero & Francesca Villano, 2023. "Predicting Building Energy Demand and Retrofit Potentials Using New Climatic Stress Indices and Curves," Energies, MDPI, vol. 16(16), pages 1-23, August.
    14. Huang, Lizhen & Bohne, Rolf André & Lohne, Jardar, 2015. "Shelter and residential building energy consumption within the 450 ppm CO2eq constraints in different climate zones," Energy, Elsevier, vol. 90(P1), pages 965-979.
    15. Kim, Jimin & Hong, Taehoon & Jeong, Jaemin & Lee, Myeonghwi & Lee, Minhyun & Jeong, Kwangbok & Koo, Choongwan & Jeong, Jaewook, 2017. "Establishment of an optimal occupant behavior considering the energy consumption and indoor environmental quality by region," Applied Energy, Elsevier, vol. 204(C), pages 1431-1443.
    16. Jin, Xiaolong & Wu, Jianzhong & Mu, Yunfei & Wang, Mingshen & Xu, Xiandong & Jia, Hongjie, 2017. "Hierarchical microgrid energy management in an office building," Applied Energy, Elsevier, vol. 208(C), pages 480-494.
    17. Jiang, Dachuan & Xiao, Weihua & Wang, Jianhua & Wang, Hao & Zhao, Yong & Li, Baoqi & Zhou, Pu, 2018. "Evaluation of the effects of one cold wave on heating energy consumption in different regions of northern China," Energy, Elsevier, vol. 142(C), pages 331-338.
    18. Piotr Narowski & Dariusz Heim & Maciej Mijakowski, 2024. "New External Design Temperatures and Geospatial Models for Poland and Central Europe for Building Heat Load Calculations," Energies, MDPI, vol. 17(16), pages 1-21, August.
    19. Pallonetto, Fabiano & De Rosa, Mattia & D’Ettorre, Francesco & Finn, Donal P., 2020. "On the assessment and control optimisation of demand response programs in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    20. Yangyi Song & Ao Du & Tong Cui, 2024. "Using the Degree-Day Method to Analyze Central Heating Energy Consumption in Cities of Northern China," Sustainability, MDPI, vol. 16(3), pages 1-14, January.

    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:377:y:2025:i:pd:s0306261924020476. 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.