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Linked Data Generation Methodology and the Geospatial Cross-Sectional Buildings Energy Benchmarking Use Case

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  • Edgar A. Martínez-Sarmiento

    (CIMNE—Centre Internacional de Metodes Numerics en Enginyeria, Edifici C1 Campus Nord UPC C/Gran Capità, S/N, Les Corts, 08034 Barcelona, Spain
    Computer Engineering and Digital Design, Polytechnic School, Campus of Cappont, UDL—Universitat de Lleida, C. de Jaume II, 69, 25001 Lleida, Spain)

  • Jose Manuel Broto

    (CIMNE—Centre Internacional de Metodes Numerics en Enginyeria, Edifici C1 Campus Nord UPC C/Gran Capità, S/N, Les Corts, 08034 Barcelona, Spain)

  • Eloi Gabaldon

    (CIMNE—Centre Internacional de Metodes Numerics en Enginyeria, Edifici C1 Campus Nord UPC C/Gran Capità, S/N, Les Corts, 08034 Barcelona, Spain)

  • Jordi Cipriano

    (CIMNE—Centre Internacional de Metodes Numerics en Enginyeria, Edifici C1 Campus Nord UPC C/Gran Capità, S/N, Les Corts, 08034 Barcelona, Spain)

  • Roberto García

    (Computer Engineering and Digital Design, Polytechnic School, Campus of Cappont, UDL—Universitat de Lleida, C. de Jaume II, 69, 25001 Lleida, Spain)

  • Stoyan Danov

    (CIMNE—Centre Internacional de Metodes Numerics en Enginyeria, Edifici C1 Campus Nord UPC C/Gran Capità, S/N, Les Corts, 08034 Barcelona, Spain)

Abstract

Cross-sectional energy benchmarking in the building domain has become crucial for policymakers, energy managers and property owners as they can compare an immovable property performance against its closest peers. For this, Key Performance Indicators (KPIs) are formulated, often relying on multiple and heterogeneous data sources which, combined, can be used to set benchmarks following normalization criteria. Geographically delimited parameters are important among these criteria because they enclose entities sharing key common characteristics the geometrical boundaries represent. Linking georeferenced heterogeneous data is not trivial, for it requires geographical aggregation, which is often taken for granted or hidden within a pre-processing activity in most energy benchmarking studies. In this article, a novel approach for Linked Data (LD) generation is presented as a methodological solution for data integration together with its application in the energy benchmarking use case. The methodology consists of eight phases that follow the best principles and recommend standards including the well-known GeoSPARQL Open Geospatial Consortium (OGC) for leveraging the geographical aggregation. Its feasibility is demonstrated by the integrated exploitation of INSPIRE-formatted cadastral data and the Buildings Performance Certifications (BPCs) available for the Catalonia region in Spain. The outcomes of this research support the adoption of the proposed methodology and provide the means for generating cross-sectional building energy benchmarking histograms from any-scale geographical aggregations on the fly.

Suggested Citation

  • Edgar A. Martínez-Sarmiento & Jose Manuel Broto & Eloi Gabaldon & Jordi Cipriano & Roberto García & Stoyan Danov, 2024. "Linked Data Generation Methodology and the Geospatial Cross-Sectional Buildings Energy Benchmarking Use Case," Energies, MDPI, vol. 17(12), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:3006-:d:1417312
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    References listed on IDEAS

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