May Tzuc, O. and Jiménez Torres, M. and Rodriguez, Carolina M. and Demesa López, F. N. and Noh Pat, F. UNSPECIFIED (2023) Cluster Analysis as a Tool for the Territorial Categorization of Energy Consumption in Buildings Based on Weather Patterns. Machine Intelligence for Smart Applications, 1105. pp. 73-91. ISSN 1860-949X
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Unsupervised machine learning Energy policy Net-zero building Territorial categorization State social housing |
| Subjects: | Subjects > Engineering |
| Divisions: | Ibero-american International University > Research > Articles and books |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 22 Jul 2026 06:33 |
| Last Modified: | 22 Jul 2026 06:33 |
| URI: | https://repositorio.funiber.org/id/eprint/28915 |
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