Using Data Sciences for Analyzing Residential Electricity Consumption in Marginalized Mexican Municipalities

In Mexico, the municipal marginalization index is used as a fundamental tool for planning and formulating public policies aimed at improving the socioeconomic conditions of the population. However, one of the limitations of this index is its exclusion of residential electricity consumption. In this regard, the present study explores the relationship between electricity consumption and the marginalization index. The research was conducted following the methodological framework of Data Science. As a result, geographic areas that cover municipalities with similar levels of residential electricity consumption were identified and characterized. It is noteworthy to mention that an inverse correlation was found between such consumption and marginalization index. Finally, these contributions could improve decision-making processes in public policies aimed at reducing economic and social inequalities, thus promoting more equitable development of the most lagging municipalities.

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Journal
International Journal of Combinatorial Optimization Problems and Informatics.
Published
2025-10-12
DOI
https://doi.org/10.61467/2007.1558.2025.v16i4.942
Primary Topic
Energy Load and Power Forecasting
Type
article
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article

Using Data Sciences for Analyzing Residential Electricity Consumption in Marginalized Mexican Municipalities

Natalie Ortiz-Guerrero, Joaquín Pérez-Ortega, Nelva Nely Almanza-Ortega, Iris Santos-González
International Journal of Combinatorial Optimization Problems and Informatics.
Energy Load and Power Forecasting
article

Using Data Sciences for Analyzing Residential Electricity Consumption in Marginalized Mexican Municipalities

Natalie Ortiz-Guerrero, Joaquín Pérez-Ortega, Nelva Nely Almanza-Ortega, Iris Santos-González
article en

Abstract

In Mexico, the municipal marginalization index is used as a fundamental tool for planning and formulating public policies aimed at improving the socioeconomic conditions of the population. However, one of the limitations of this index is its exclusion of residential electricity consumption. In this regard, the present study explores the relationship between electricity consumption and the marginalization index. The research was conducted following the methodological framework of Data Science. As a result, geographic areas that cover municipalities with similar levels of residential electricity consumption were identified and characterized. It is noteworthy to mention that an inverse correlation was found between such consumption and marginalization index. Finally, these contributions could improve decision-making processes in public policies aimed at reducing economic and social inequalities, thus promoting more equitable development of the most lagging municipalities.

International Journal of Combinatorial Optimization Problems and Informatics.Vol. 16(4)
Tecnológico Nacional de México (MX), Centro Nacional de Investigación y Desarrollo Tecnológico (MX), Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (EC)
Openalex Percentile: Top 72%
Energy Load and Power Forecasting
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