IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i2p652-d1317435.html
   My bibliography  Save this article

The Open Data Potential for the Geospatial Characterisation of Building Stock on an Urban Scale: Methodology and Implementation in a Case Study

Author

Listed:
  • Cristina Villanueva-Díaz

    (ENEDI Research Group, Department of Energy Engineering, Faculty of Engineering Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain)

  • Milagros Álvarez-Sanz

    (ENEDI Research Group, Department of Energy Engineering, Faculty of Engineering Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain)

  • Álvaro Campos-Celador

    (ENEDI Research Group, Department of Energy Engineering, Faculty of Engineering Gipuzkoa, University of the Basque Country (UPV/EHU), 20600 Eibar, Spain)

  • Jon Terés-Zubiaga

    (ENEDI Research Group, Department of Energy Engineering, Faculty of Engineering Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain)

Abstract

Energy renovation in buildings is one of the major challenges for the decarbonisation of the building stock. To effectively prioritise decision making regarding the adoption of the most efficient solutions and strategies, it is imperative to develop agile methods to determine the energy performance of buildings on an urban scale, in order to evaluate the impact of these improvements. In this regard, the data collection for feeding building energy models plays a key role in the accuracy and reliability of this issue, and the significant increase in recent years of available data from open data sources offers great potential in this respect. Thus, this study focuses on proposing a systematised and automated method for obtaining information from open data sources so as to obtain the most relevant geometric and thermal characteristics of residential buildings on an urban scale. The criteria for selecting the parameters to be obtained are based on their potential use as input data in different energy demand models aimed at assessing the energy performance of the building stock in a given area and, eventually, to evaluate the potential for improvement and the mitigation of different strategies. Geometric characterisation relies on obtaining and processing open data from cadastres to extract envelope surfaces categorised by orientation through QGIS (Free and Open Source Geographic Information System). For thermal characterisation, an automated process assigns different parameter-based information obtained from cadastral data, such as the year of construction. Finally, the applicability of the method is demonstrated through its implementation in the case study of Bilbao (Spain). The obtained results show that, although additional data should be collected when a detailed analysis of a building or building cluster has to be carried out, the existing open data can provide a first approximation, providing a first global view of the building stock in a region. It demonstrates the usability of the proposed method as an effective way to obtain and process these relevant data.

Suggested Citation

  • Cristina Villanueva-Díaz & Milagros Álvarez-Sanz & Álvaro Campos-Celador & Jon Terés-Zubiaga, 2024. "The Open Data Potential for the Geospatial Characterisation of Building Stock on an Urban Scale: Methodology and Implementation in a Case Study," Sustainability, MDPI, vol. 16(2), pages 1-24, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:652-:d:1317435
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/2/652/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/2/652/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fabrizio Cumo & Federica Giustini & Elisa Pennacchia & Carlo Romeo, 2020. "Support Decision Tool for Sustainable Energy Requalification the Existing Residential Building Stock. The Case Study of Trevignano Romano," Energies, MDPI, vol. 14(1), pages 1-16, December.
    2. George M. Stavrakakis & Panagiotis L. Zervas & Konstantinos Terzis & Panagiotis Langouranis & Panagiota Saranti & Yorgos J. Stephanedes, 2023. "Exploitation of Mediterranean Cooperation Projects’ Tools for the Development of Public Buildings’ Energy Efficiency Plans at Local Level: A Case Study in Greece," Energies, MDPI, vol. 16(8), pages 1-33, April.
    3. Yang, Xining & Hu, Mingming & Heeren, Niko & Zhang, Chunbo & Verhagen, Teun & Tukker, Arnold & Steubing, Bernhard, 2020. "A combined GIS-archetype approach to model residential space heating energy: A case study for the Netherlands including validation," Applied Energy, Elsevier, vol. 280(C).
    4. Hanan Ibrahim & Ahmed Z. Khan & Shady Attia & Yehya Serag, 2021. "Classification of heritage residential building stock and defining sustainable retrofitting scenarios in Khedivial Cairo," ULB Institutional Repository 2013/326195, ULB -- Universite Libre de Bruxelles.
    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. Alexander Jüstel & Elias Humm & Eileen Herbst & Frank Strozyk & Peter Kukla & Rolf Bracke, 2024. "Unveiling the Spatial Distribution of Heat Demand in North-West-Europe Compiled with National Heat Consumption Data," Energies, MDPI, vol. 17(2), pages 1-36, January.
    2. Marion Eva Pauline Perney & Gigliola D’Angelo, 2023. "Local Governance Support Tools for Disaster Risk Reduction and Climate Adaptation Strategies: The EU Contribution in the Case Study of the Municipality of Naples," Sustainability, MDPI, vol. 15(15), pages 1-19, July.
    3. George M. Stavrakakis & Dimitris Bakirtzis & Korina-Konstantina Drakaki & Sofia Yfanti & Dimitris Al. Katsaprakakis & Konstantinos Braimakis & Panagiotis Langouranis & Konstantinos Terzis & Panagiotis, 2024. "Application of the Typology Approach for Energy Renovation Planning of Public Buildings’ Stocks at the Local Level: A Case Study in Greece," Energies, MDPI, vol. 17(3), pages 1-30, January.
    4. Yamaguchi, Yohei & Shoda, Yuto & Yoshizawa, Shinya & Imai, Tatsuya & Perwez, Usama & Shimoda, Yoshiyuki & Hayashi, Yasuhiro, 2023. "Feasibility assessment of net zero-energy transformation of building stock using integrated synthetic population, building stock, and power distribution network framework," Applied Energy, Elsevier, vol. 333(C).
    5. Hanan S.S. Ibrahim & Ahmed Z. Khan & Waqas Ahmed Mahar & Shady Attia & Yehya Serag, 2021. "Assessment of Passive Retrofitting Scenarios in Heritage Residential Buildings in Hot, Dry Climates," Energies, MDPI, vol. 14(11), pages 1-27, June.
    6. Agbonaye, Osaru & Keatley, Patrick & Huang, Ye & Ademulegun, Oluwasola O. & Hewitt, Neil, 2021. "Mapping demand flexibility: A spatio-temporal assessment of flexibility needs, opportunities and response potential," Applied Energy, Elsevier, vol. 295(C).
    7. Yang, Xining & Hu, Mingming & Tukker, Arnold & Zhang, Chunbo & Huo, Tengfei & Steubing, Bernhard, 2022. "A bottom-up dynamic building stock model for residential energy transition: A case study for the Netherlands," Applied Energy, Elsevier, vol. 306(PA).
    8. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
    9. Athanasia Apostolopoulou & Mingyu Zhu & Jiayi Jin, 2023. "Parametric Assessment of Building Heating Demand for Different Levels of Details and User Comfort Levels: A Case Study in London, UK," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    10. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    11. Abdulraheem Salaymeh & Irene Peters & Stefan Holler, 2024. "Factoring Building Refurbishment and Climatic Effect into Heat Demand Assessments and Forecasts: Case Study and Open Datasets for Germany," Energies, MDPI, vol. 17(3), pages 1-20, January.
    12. Tatjana Vilutienė & Rasa Džiugaitė-Tumėnienė & Diana Kalibatienė & Darius Kalibatas, 2021. "How BIM Contributes to a Building’s Energy Efficiency throughout Its Whole Life Cycle: Systematic Mapping," Energies, MDPI, vol. 14(20), pages 1-27, October.
    13. Friebe, Maximilian & Karasu, Arda & Kriegel, Martin, 2023. "Methodology to compare and optimize district heating and decentralized heat supply for energy transformation on a municipality level," Energy, Elsevier, vol. 282(C).
    14. Li, Shengping & Rismanchi, Behzad & Aye, Lu, 2022. "A simulation-based bottom-up approach for analysing the evolution of residential buildings’ material stocks and environmental impacts – A case study of Inner Melbourne," Applied Energy, Elsevier, vol. 314(C).
    15. Shen, Pengyuan & Wang, Huilong, 2024. "Archetype building energy modeling approaches and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).

    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:gam:jsusta:v:16:y:2024:i:2:p:652-:d:1317435. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.