Identifying urban energy-vulnerable areas: a machine learning approach Aguilar Aguilera, Antonio Jesús Guerrero Rivera, María Fernanda De la Hoz Torres, María Luisa Energy vulnerability Energy poverty Dwellings Machine learning Socioeconomic data Building renovation wave Energy performance certificate This study was funded by the Spanish Ministry of Science and Innovation, under the research project PID2021-122437OA-I00 “Positive Energy Buildings Potential for Climate Change Adaptation and Energy Poverty Mitigation (+ENERPOT)”. Funding for open access charge: Universidad de Granada / CBUA. María Luisa de la Hoz-Torres wishes to acknowledge the support of the MICIU/AEI/10.13039/501100011033 and by European Union NextGeneration EU/PRTR under a Juan de la Cierva post-doctoral contract (JDC2022-049561-I). Access to energy services is essential for preserving health and well-being. However, energy poverty is a challenge affecting millions of citizens worldwide, which could even worsen due to the predicted severity of climate change. Energy poverty vulnerability and social problems are often linked to energy-inefficient buildings. Thus, identifying energy-inefficient dwellings in energy-vulnerable urban areas is crucial for formulating and implementing effective public policies. Consequently, this study proposes a multidimensional methodological approach to determine these urban areas and support decision-making to develop public policies that can help lift dwellings out of or prevent them from falling into energy poverty. The suggested methodology utilizes public data from existing databases and applies unsupervised machine–learning classification algorithms. Applying such methodology to the case study of Seville identified different clusters of urban areas with similar characteristics, providing key information for creating specific public policies tailored to the needs of each area and community. The study’s findings support Building Renovation Wave strategies to improve energy efficiency in dwellings, define specific policies for access to financial resources for low-income families, and provide personalized support for vulnerable populations. 2025-06-02T07:19:36Z 2025-06-02T07:19:36Z 2025-05-29 journal article Aguilar, A. J., Guerrero-Rivera, M. F., & de la Hoz-Torres, M. L. (2025). Identifying urban energy-vulnerable areas: a machine learning approach. Journal of Building Engineering, 113047. https://doi.org/10.1016/j.jobe.2025.113047 https://hdl.handle.net/10481/104410 10.1016/j.jobe.2025.113047 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier