On February 12, Milagros Álvarez Sanz, ENEDI researcher, defended her doctoral thesis titled: Developing Simple Models for Analysing Energy Performance of Buildings at Neighbourhood Scale Using Geographic Information Systems (GIS).

This doctoral thesis develops and validates simplified models to estimate heating demand at the neighbourhood scale, using Geographic Information Systems (GIS), publicly available data, and key design and operational parameters. Given the impact of the building sector on global energy consumption, the research explores algorithms that balance accuracy, computational efficiency, and ease of use. Statistical and machine learning methods are compared, highlighting their ability to improve heating demand estimation. The developed models enable the assessment of energy retrofit strategies in urban environments and are integrated into GIS platforms through a case study. The findings conclude that these models serve as valuable tools for urban planning and policy-making, supporting energy consumption reduction and climate change mitigation.

Likewise, as a result of the research activity derived from this thesis, the following articles have been published:

  • Ranking building design and operation parameters for residential heating demand forecasting with machine learning. Milagros Álvarez-Sanz, Felicia Agatha Satriya, Jon Terés-Zubiaga, Álvaro Campos-Celador, Unai Bermejo. Journal of Building Engineering, Volume 86, 1 June 2024, 108817

  • The Open Data Potential for the Geospatial Characterisation of Building Stock on an Urban Scale: Methodology and Implementation in a Case Study. Cristina Villanueva-Díaz, Milagros Álvarez-Sanz, Álvaro Campos-Celador, Jon Terés-Zubiaga.  Sustainability, Volume 16, Issue 2, January 2024.