Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach
The Brazilian residential sector is one of the largest consumers of electricity, making residential energy consumption a critical component of national energy systems. Electricity consumption patterns in this sector are closely associated with household appliance ownership and, consequently, with so...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2026
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| author | Ferreira, Lucas Camaz Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane López Gonzales, Javier Linkolk Tocto Cano, Esteban Centurion, Lennin |
| author_browse | Centurion, Lennin Ferreira, Lucas Camaz Leite Coelho da Silva, Felipe López Gonzales, Javier Linkolk Tocto Cano, Esteban da Silva Cordeiro, Josiane |
| author_facet | Ferreira, Lucas Camaz Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane López Gonzales, Javier Linkolk Tocto Cano, Esteban Centurion, Lennin |
| author_sort | Ferreira, Lucas Camaz |
| collection | Producción científica |
| description | The Brazilian residential sector is one of the largest consumers of electricity, making residential energy consumption a critical component of national energy systems. Electricity consumption patterns in this sector are closely associated with household appliance ownership and, consequently, with socioeconomic status. For residential energy planning to operate more equitably and efficiently, it is essential that consumption analyses be aligned with the socioeconomic conditions of the population. This study examines the role of socioeconomic variables in residential energy planning through the application of supervised machine learning algorithms within a data-driven socioeconomic segmentation framework. Decision trees, support vector machines, and artificial neural networks were implemented using data from the Brazilian residential sector to evaluate model performance and to determine the extent to which household socioeconomic status can be inferred from variables related to appliance ownership and electricity consumption characteristics. The results showed that household appliances, such as refrigerators, microwave ovens, and air conditioners, exhibited substantial predictive power in relation to socioeconomic status, thus improving the interpretation and understanding of residential energy consumption from a multidimensional perspective. The neural network model achieved the highest predictive performance. By enabling data-driven socioeconomic segmentation based on observable electricity consumption patterns, this approach provides relevant insights for residential energy planning and contributes to more targeted and equitable energy policy design, supporting Sustainable Development Goal 7 on Affordable and Clean Energy and Sustainable Development Goal 10 on Reduced Inequalities. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/389 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/3892026-11-02T12:57:04ZData-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning ApproachFerreira, Lucas CamazLeite Coelho da Silva, Felipeda Silva Cordeiro, JosianeLópez Gonzales, Javier LinkolkTocto Cano, EstebanCenturion, Lenninmachine learningartificial neural networkdata-driven socioeconomicenergiesThe Brazilian residential sector is one of the largest consumers of electricity, making residential energy consumption a critical component of national energy systems. Electricity consumption patterns in this sector are closely associated with household appliance ownership and, consequently, with socioeconomic status. For residential energy planning to operate more equitably and efficiently, it is essential that consumption analyses be aligned with the socioeconomic conditions of the population. This study examines the role of socioeconomic variables in residential energy planning through the application of supervised machine learning algorithms within a data-driven socioeconomic segmentation framework. Decision trees, support vector machines, and artificial neural networks were implemented using data from the Brazilian residential sector to evaluate model performance and to determine the extent to which household socioeconomic status can be inferred from variables related to appliance ownership and electricity consumption characteristics. The results showed that household appliances, such as refrigerators, microwave ovens, and air conditioners, exhibited substantial predictive power in relation to socioeconomic status, thus improving the interpretation and understanding of residential energy consumption from a multidimensional perspective. The neural network model achieved the highest predictive performance. By enabling data-driven socioeconomic segmentation based on observable electricity consumption patterns, this approach provides relevant insights for residential energy planning and contributes to more targeted and equitable energy policy design, supporting Sustainable Development Goal 7 on Affordable and Clean Energy and Sustainable Development Goal 10 on Reduced Inequalities.Articlehttps://www.mdpi.com/1996-1073/19/9/2229/pdf?version=1777970801https://doi.org/10.3390/en1909222910.3390/en19092229https://hdl.handle.net/20.500.15037/389W7160335067enEnergies1996-10732026-05-052026-06-13T08:12:29Z2026-06-13T08:12:29Z |
| spellingShingle | machine learning artificial neural network data-driven socioeconomic energies Ferreira, Lucas Camaz Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane López Gonzales, Javier Linkolk Tocto Cano, Esteban Centurion, Lennin Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach |
| title | Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach |
| title_full | Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach |
| title_fullStr | Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach |
| title_full_unstemmed | Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach |
| title_short | Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach |
| title_sort | data driven socioeconomic segmentation for residential energy planning a machine learning approach |
| topic | machine learning artificial neural network data-driven socioeconomic energies |
| topic_facet | machine learning artificial neural network data-driven socioeconomic energies |
| url | https://www.mdpi.com/1996-1073/19/9/2229/pdf?version=1777970801 https://doi.org/10.3390/en19092229 https://hdl.handle.net/20.500.15037/389 |
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