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Cluster Analysis as a Tool for the Territorial Categorization of Energy Consumption in Buildings Based on Weather Patterns

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Machine Intelligence for Smart Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1105))

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Abstract

This book chapter explores the application of k-means, an unsupervised learning technique designed to allow the categorization of patterns and statistical and geographic indicators of energy consumption in various climatic regions of Mexico. It investigates the relationship between energy consumption and climatic and operational patterns in a case study of State Social Housing. The k-means results demonstrate how the distribution of the groups obeys temperature and relative humidity patterns, which can be visualized using Geographic Information Systems software. This methodology has broad implications for future studies on thermal comfort, energy poverty, and pollutant emissions and lays the foundation for replicable research on energy efficiency in housing and other related fields.

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Acknowledgements

Coauthor Mario Jiménez Torres is thankful for the financial support granted by CONAHCYT (CVU No. 930301, scholarship no. 785382) to pursue a doctoral grand in Universidad Autónoma de Yucatán, México. This work is part of project 053/UAC/2022 and is a derivative product of Thematic Network 722RT0135 “Red Iberoamericana de Pobreza Energética y Bienestar Ambiental” (RIPEBA), which provided financial support through the CYTED Program’s 2021 call for Thematic Networks.

Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Autónoma de Campeche, Humberto Lanz Cárdenas y Unidad Habitacional Ecológica Ambiental, Col. Ex Hacienda Kalá, C.P. 24085, San Franciso de Campeche, Campeche, México

    O. May Tzuc & F. Noh Pat

  2. Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes por Anillo Periférico Norte, Apdo. Postal 150, Cordemex, Mérida, Yucatán, México

    M. Jiménez Torres

  3. Programa de Arquitectura, Universidad Piloto de Colombia, Carrera 9ª # 45ª-44, Sede F, C.P. 110231, Bogotá, Colombia

    Carolina M. Rodriguez

  4. División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Pachuca, Col. Venta Prieta, Carretera México-Pachuca Km. 87.5, C.P. 42083, Pachuca de Soto, Hidalgo, México

    F. N. Demesa López

  5. Departamento de Ingeniería y Proyectos, Universidad Internacional Iberoamericana, Col. IMI III, C.P. 24560, San Francisco de Campeche, Campeche, México

    M. Jiménez Torres

Authors
  1. O. May Tzuc
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  2. M. Jiménez Torres
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  3. Carolina M. Rodriguez
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  4. F. N. Demesa López
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  5. F. Noh Pat
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Corresponding author

Correspondence to O. May Tzuc .

Editor information

Editors and Affiliations

  1. Moulay Ismail University, Meknes, Morocco

    Amina Adadi

  2. ENSA, Sidi Mohamed Ben Abdellah University, Fès, Morocco

    Saad Motahhir

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May Tzuc, O., Jiménez Torres, M., Rodriguez, C.M., Demesa López, F., Noh Pat, F. (2023). Cluster Analysis as a Tool for the Territorial Categorization of Energy Consumption in Buildings Based on Weather Patterns. In: Adadi, A., Motahhir, S. (eds) Machine Intelligence for Smart Applications. Studies in Computational Intelligence, vol 1105. Springer, Cham. https://doi.org/10.1007/978-3-031-37454-8_4

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