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Gerard Mor Martinez

International Center for Numerical Methods in Engineering, Building Energy and Environment Group (CIMNE-BEE)

Spain

SCHOLARLY PAPERS

3

DOWNLOADS

115

TOTAL CITATIONS

1

Scholarly Papers (3)

1.

A Data-Driven Method for Unsupervised Electricity Consumption Characterisation at the District Level and Beyond

Number of pages: 42 Posted: 24 Jun 2021
International Center for Numerical Methods in Engineering, Building Energy and Environment Group (CIMNE-BEE), International Center for Numerical Methods in Engineering, Building Energy and Environment Group (CIMNE-BEE), Joint Research Center of the European Commission, Joint Research Center of the European Commission, Joint Research Center of the European Commission, International Center for Numerical Methods in Engineering, Building Energy and Environment Group (CIMNE-BEE), International Center for Numerical Methods in Engineering, Building Energy and Environment Group (CIMNE-BEE) and University of Lleida
Downloads 63 (942,565)
Citation 1

Abstract:

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2.

The BIGG Ontology: A Semantic Framework for Integrating Urban Multi-sourced Geospatial Data

Number of pages: 31 Posted: 21 Jan 2026
affiliation not provided to SSRN, affiliation not provided to SSRN, International Center for Numerical Methods in Engineering, Building Energy and Environment Group (CIMNE-BEE), affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, Universidad Politécnica de Madrid, International Center for Numerical Methods in Engineering, Building Energy and Environment Group (CIMNE-BEE), affiliation not provided to SSRN, Concordia University and Universidad Politécnica de Cataluña
Downloads 29 (1,372,601)

Abstract:

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buildings, urban planning, Geospatial data, Territorial analysis, Predictive modeling, Ontology, Data interoperability, Energy

3.

Baseline modelling of tertiary buildings for large-scale energy analysis applications

Number of pages: 18 Posted: 08 Sep 2025
affiliation not provided to SSRN, International Center for Numerical Methods in Engineering, Building Energy and Environment Group (CIMNE-BEE), affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, International Center for Numerical Methods in Engineering, Building Energy and Environment Group (CIMNE-BEE) and Universidad Politécnica de Cataluña
Downloads 23 (1,411,822)

Abstract:

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Energy Efficiency, Machine learning, Energy baseline model, Data-driven, Large-scale analysis, Low-Data methods