Data Analysis of Electricity Service in Colombia's Non-Interconnected Zones through Different Clustering Techniques
Energy determines the social, economic, and environmental aspects that enable the advancement of communities. For this reason, this paper aims to analyze the quality of the energy service in the Non-Interconnected Zones (NIZ) of Colombia. For this purpose, clustering techniques (K-means, K-medoids,...
- Autores:
-
Colmenares Quintero, Ramón Fernando
Maestre Góngora, Gina Paola
Baquero Almazo, Marieth
Stansfield, Kim
Colmenares Quintero, Juan Carlos
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2022
- Institución:
- Universidad Cooperativa de Colombia
- Repositorio:
- Repositorio UCC
- Idioma:
- OAI Identifier:
- oai:repository.ucc.edu.co:20.500.12494/48910
- Palabra clave:
- Minería de datos
Clustering
Partición de Clusters
Clusters jerárquicos
Sistemas energéticos
Data mining
Clustering
Partitioning clusters
Hierarchical clusters
Energy service
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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Data Analysis of Electricity Service in Colombia's Non-Interconnected Zones through Different Clustering Techniques |
| title |
Data Analysis of Electricity Service in Colombia's Non-Interconnected Zones through Different Clustering Techniques |
| spellingShingle |
Data Analysis of Electricity Service in Colombia's Non-Interconnected Zones through Different Clustering Techniques Minería de datos Clustering Partición de Clusters Clusters jerárquicos Sistemas energéticos Data mining Clustering Partitioning clusters Hierarchical clusters Energy service |
| title_short |
Data Analysis of Electricity Service in Colombia's Non-Interconnected Zones through Different Clustering Techniques |
| title_full |
Data Analysis of Electricity Service in Colombia's Non-Interconnected Zones through Different Clustering Techniques |
| title_fullStr |
Data Analysis of Electricity Service in Colombia's Non-Interconnected Zones through Different Clustering Techniques |
| title_full_unstemmed |
Data Analysis of Electricity Service in Colombia's Non-Interconnected Zones through Different Clustering Techniques |
| title_sort |
Data Analysis of Electricity Service in Colombia's Non-Interconnected Zones through Different Clustering Techniques |
| dc.creator.fl_str_mv |
Colmenares Quintero, Ramón Fernando Maestre Góngora, Gina Paola Baquero Almazo, Marieth Stansfield, Kim Colmenares Quintero, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Colmenares Quintero, Ramón Fernando Maestre Góngora, Gina Paola Baquero Almazo, Marieth Stansfield, Kim Colmenares Quintero, Juan Carlos |
| dc.subject.none.fl_str_mv |
Minería de datos Clustering Partición de Clusters Clusters jerárquicos Sistemas energéticos |
| topic |
Minería de datos Clustering Partición de Clusters Clusters jerárquicos Sistemas energéticos Data mining Clustering Partitioning clusters Hierarchical clusters Energy service |
| dc.subject.other.none.fl_str_mv |
Data mining Clustering Partitioning clusters Hierarchical clusters Energy service |
| description |
Energy determines the social, economic, and environmental aspects that enable the advancement of communities. For this reason, this paper aims to analyze the quality of the energy service in the Non-Interconnected Zones (NIZ) of Colombia. For this purpose, clustering techniques (K-means, K-medoids, divisive analysis clustering, and heatmaps) are applied for data analysis in the context of the NIZ to identify patterns or hidden information in the Colombian government data related to the state of the electricity service in these localities during the years 2019–2020. A descriptive statistical analysis and validation of the results of the clustering techniques is also carried out using R software. Through the implementation of clustering algorithms such as K-means, K-medoids, and divisive analysis clustering, potential areas for the development of renewable and alternative energy projects are identified, considering places with deficiencies in their current electricity service, higher consumption, or places with very low daily hours of electricity service. Additionally, relationships were identified in the dataset that can be considered as tools that would support decision-making for academia and industry, as well as the definition of guidelines or strategies from the government to improve energy efficiency and quality for these places, and consequently, the living conditions of the residents of Colombia’s NIZs. |
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2022 |
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2022-10 |
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2023-03-10T16:48:59Z |
| dc.date.available.none.fl_str_mv |
2023-03-10T16:48:59Z |
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Artículos Científicos |
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info:eu-repo/semantics/article |
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1996-1073 |
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https://doi.org/10.3390/en15207644 https://hdl.handle.net/20.500.12494/48910 |
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Colmenares-Quintero, R.F.; Maestre-Gongora, G.; Baquero-Almazo, M.; Stansfield, K.E.; Colmenares-Quintero, J.C. Data Analysis of Electricity Service in Colombia’s Non-Interconnected Zones through Different Clustering Techniques. Energies 2022, 15, 7644. https://doi.org/10.3390/en15207644 Colmenares-Quintero RF, Maestre-Gongora G, Baquero-Almazo M, Stansfield KE, Colmenares-Quintero JC. Data Analysis of Electricity Service in Colombia’s Non-Interconnected Zones through Different Clustering Techniques. Energies. 2022; 15(20):7644. https://doi.org/10.3390/en15207644 Colmenares-Quintero, Ramón Fernando, Gina Maestre-Gongora, Marieth Baquero-Almazo, Kim E. Stansfield, and Juan Carlos Colmenares-Quintero. 2022. "Data Analysis of Electricity Service in Colombia’s Non-Interconnected Zones through Different Clustering Techniques" Energies 15, no. 20: 7644. https://doi.org/10.3390/en15207644 |
| identifier_str_mv |
1996-1073 Colmenares-Quintero, R.F.; Maestre-Gongora, G.; Baquero-Almazo, M.; Stansfield, K.E.; Colmenares-Quintero, J.C. Data Analysis of Electricity Service in Colombia’s Non-Interconnected Zones through Different Clustering Techniques. Energies 2022, 15, 7644. https://doi.org/10.3390/en15207644 Colmenares-Quintero RF, Maestre-Gongora G, Baquero-Almazo M, Stansfield KE, Colmenares-Quintero JC. Data Analysis of Electricity Service in Colombia’s Non-Interconnected Zones through Different Clustering Techniques. Energies. 2022; 15(20):7644. https://doi.org/10.3390/en15207644 Colmenares-Quintero, Ramón Fernando, Gina Maestre-Gongora, Marieth Baquero-Almazo, Kim E. Stansfield, and Juan Carlos Colmenares-Quintero. 2022. "Data Analysis of Electricity Service in Colombia’s Non-Interconnected Zones through Different Clustering Techniques" Energies 15, no. 20: 7644. https://doi.org/10.3390/en15207644 |
| url |
https://doi.org/10.3390/en15207644 https://hdl.handle.net/20.500.12494/48910 |
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https://www.mdpi.com/1996-1073/15/20/7644 |
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Energies |
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Colmenares-Quintero, R.F.; Latorre-Noguera, L.F.; Rojas, N.; Kolmsee, K.; Stansfield, K.E.; Colmenares-Quintero, J.C. Computa tional Framework for the Selection of Energy Solutions in Indigenous Communities in Colombia: Kanalitojo Case Study. Cogent Eng. 2021, 8, 1926406. Ochoa, L.L.; Paredes, K.R.; Tejada, J.E. Estudio Comparativo de Técnicas no Supervisadas de Minería de Datos para Segmentación de Alumnos. In Global Partnerships for Development and Engineering Education, Proceedings of the 15th LACCEI International Multi-Conference for Engineering, Education and Technology, Boca Raton, FL, USA, 19–21 July 2017; Latin American and Caribbean Consortium of Engineering Institutions: Boca Raton, FL, USA, 2017; p. 115. ISBN 978-0-9993443-0-9. Razak, M.A.; Yakub, F.; Sulaiman, N.N.I.; Rashid, A.M.Z.; Shaikh Salim, S.A.Z.; Rasid, A.Z.; Abu, A. Energy Consumption Clustering Analysis in Residential Building. In Proceedings of the Intelligent Manufacturing and Mechatronics; Jamaludin, Z., Ali Mokhtar, M.N., Eds.; Springer: Singapore, 2020; pp. 436–450. [CrossRef] Saxena, A.; Prasad, M.; Gupta, A.; Bharill, N.; Patel, O.P.; Tiwari, A.; Er, M.J.; Ding, W.; Lin, C.-T. A Review of Clustering Techniques and Developments. Neurocomputing 2017, 267, 664–681. Chitra, K.; Maheswari, D. A Comparative Study of Various Clustering Algorithms in Data Mining. Int. J. Comput. Sci. Mob. Comput. 2017, 6, 109–115 Amat Rodrigo, J. RPubs-Clustering y Heatmaps: Aprendizaje No Supervisado Con R. Available online: https://rpubs.com/ Joaquin_AR/310338 (accessed on 5 December 2021) Rodriguez, M.Z.; Comin, C.H.; Casanova, D.; Bruno, O.M.; Amancio, D.R.; Costa, L.D.; Rodrigues, F.A. Clustering Algorithms: A Comparative Approach. PLoS ONE 2019, 14, e0210236. [ Tizón Galisteo, D. Big Data Clustering. Master’s Thesis, UNED, Madrid, Spain, 2017. Gostkowski, M.; Rokicki, T.; Ochnio, L.; Koszela, G.; Wojtczuk, K.; Ratajczak, M.; Szczepaniuk, H.; Bórawski, P.; Bełdycka Bórawska, A. Clustering Analysis of Energy Consumption in the Countries of the Visegrad Group. Energies 2021, 14, 5612. [CrossRef] Li, Y.; Yang, J.; Jiang, X. Study on Clustering Analysis of Building Energy Consumption Data. IOP Conf. Ser. Earth Environ. Sci. 2021, 676, 012061. [CrossRef] Liu, X.; Ding, Y.; Tang, H.; Xiao, F. A Data Mining-Based Framework for the Identification of Daily Electricity Usage Patterns and Anomaly Detection in Building Electricity Consumption Data. Energy Build. 2021, 231, 110601. [CrossRef] Ramos, S.; Soares, J.; Cembranel, S.S.; Tavares, I.; Foroozandeh, Z.; Vale, Z.; Fernandes, R. Data Mining Techniques for Electricity Customer Characterization. Procedia Comput. Sci. 2021, 186, 475–488. [CrossRef] Kapousouz, E.; Seyrfar, A.; Derrible, S.; Ataei, H. Chapter 5-A Clustering Analysis of Energy and Water Consumption in U.S. States from 1985 to 2015. In Data Science Applied to Sustainability Analysis; Dunn, J., Balaprakash, P., Eds.; Elsevier: Amsterdam, The Netherlands, 2021; pp. 81–108. ISBN 978-0-12-817976-5 IPSE, IPSE–Energía Que Nos Conecta. Available online: https://ipse.gov.co/ (accessed on 18 May 2022) Colmenares-Quintero, R.F.; Maestre-Gongora, G.P.; Pacheco-Moreno, L.J.; Rojas, N.; Stansfield, K.E.; Colmenares-Quintero, J.C. Analysis of the Energy Service in Non-Interconnected Zones of Colombia Using Business Intelligence. Cogent Eng. 2021, 8, 1907970. [CrossRef] R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020 Abusleme, C. ¿Por qué los gobiernos promueven estrategias de datos abiertos? Los casos de México, Chile y Colombia. Rev. Estud. Políticas Públicas 2020, 6, 20–41. [CrossRef] Maestre-Gongora, G.; Rangel-Carrillo, A.; Osorio-Sanabria, M. The Value of Open Data Government: A Quality Assessment Approach. Rev. Investig. Desarro. E Innov. 2021, 11, 507–518. [CrossRef] IPSE Aumenta en un 5.9% la Energía Registrada en las Localidades de las Zonas No Interconectadas según Informe de Telemetría. Available online: https://ipse.gov.co/blog/2021/11/26/aumenta-en-un-5-9-la-energia-registrada-en-las-localidades-de-las zonas-no-interconectadas-segun-informe-de-telemetria/ (accessed on 27 September 2022). Reddy, C.K.; Vinzamuri, B. A Survey of Partitional and Hierarchical Clustering Algorithms. In Data Clustering; Chapman and Hall/CRC: Boca Raton, FL, USA, 2014; ISBN 978-1-315-37351-5 Carvajal-Romo, G.; Valderrama-Mendoza, M.; Rodríguez-Urrego, D.; Rodríguez-Urrego, L. Assessment of Solar and Wind Energy Potential in La Guajira, Colombia: Current Status, and Future Prospects. Sustain. Energy Technol. Assess. 2019, 36, 100531. [CrossRef] López, A.R.; Krumm, A.; Schattenhofer, L.; Burandt, T.; Montoya, F.C.; Oberländer, N.; Oei, P.-Y. Solar PV Generation in Colombia A Qualitative and Quantitative Approach to Analyze the Potential of Solar Energy Market. Renew. Energy 2020, 148, 1266–1279. [CrossRef] Villegas-Quiceno, A.P.; Aristizabal-Tique, V.H.; Arbelaez-Pérez, O.F.; Colmenares-Quintero, R.F.; Vélez-Hoyos, F.J. Development of Riverine Hydrokinetic Energy Systems in Colombia and Other World Regions: A Review of Case Studies. DYNA 2021, 88, 256–264. [CrossRef] Vides-Prado, A.; Camargo, E.O.; Vides-Prado, C.; Orozco, I.H.; Chenlo, F.; Candelo, J.E.; Sarmiento, A.B. Techno-Economic Feasibility Analysis of Photovoltaic Systems in Remote Areas for Indigenous Communities in the Colombian Guajira. Renew. Sustain. Energy Rev. 2018, 82, 4245–4255. [CrossRef] Sy, S.A.; Mokaddem, L. Energy Poverty in Developing Countries: A Review of the Concept and Its Measurements. Energy Res. Soc. Sci. 2022, 89, 102562. [CrossRef] Hiemstra-van der Horst, G.; Hovorka, A.J. Reassessing the “Energy Ladder”: Household Energy Use in Maun, Botswana. Energy Policy 2008, 36, 3333–3344. [CrossRef] Benavides-Castillo, J.M.; Carmona-Parra, J.A.; Rojas, N.; Stansfield, K.E.; Colmenares-Quintero, J.C.; Colmenares-Quintero, R.F. Framework to Design Water-Energy Solutions Based on Community Perceptions: Case Study from a Caribbean Coast Community in Colombia. Cogent Eng. 2021, 8, 1905232. [CrossRef] Fisher, U.; Sugarmen, C.; Ring, A.; Sinai, J. Gas Turbine “Solarization”-Modifications for Solar/Fuel Hybrid Operation. J. Sol. Energy Eng. 2004, 126, 872–878. [CrossRef] Prieto, A.V.; García-Estévez, J.; Ariza, J.F. On the Relationship between Mining and Rural Poverty: Evidence for Colombia. Resour. Policy 2022, 75, 102443. [CrossRef] Atlas Interactivo-Radiación IDEAM. Available online: http://atlas.ideam.gov.co/visorAtlasRadiacion.html (accessed on 3 September 2021) Atlas Interactivo-Vientos-IDEAM. Available online: http://atlas.ideam.gov.co/visorAtlasVientos.html (accessed on 3 September 2021) |
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Colmenares Quintero, Ramón FernandoMaestre Góngora, Gina PaolaBaquero Almazo, MariethStansfield, KimColmenares Quintero, Juan Carlos152023-03-10T16:48:59Z2023-03-10T16:48:59Z2022-101996-1073https://doi.org/10.3390/en15207644https://hdl.handle.net/20.500.12494/48910Colmenares-Quintero, R.F.; Maestre-Gongora, G.; Baquero-Almazo, M.; Stansfield, K.E.; Colmenares-Quintero, J.C. Data Analysis of Electricity Service in Colombia’s Non-Interconnected Zones through Different Clustering Techniques. Energies 2022, 15, 7644. https://doi.org/10.3390/en15207644Colmenares-Quintero RF, Maestre-Gongora G, Baquero-Almazo M, Stansfield KE, Colmenares-Quintero JC. Data Analysis of Electricity Service in Colombia’s Non-Interconnected Zones through Different Clustering Techniques. Energies. 2022; 15(20):7644. https://doi.org/10.3390/en15207644Colmenares-Quintero, Ramón Fernando, Gina Maestre-Gongora, Marieth Baquero-Almazo, Kim E. Stansfield, and Juan Carlos Colmenares-Quintero. 2022. "Data Analysis of Electricity Service in Colombia’s Non-Interconnected Zones through Different Clustering Techniques" Energies 15, no. 20: 7644. https://doi.org/10.3390/en15207644Energy determines the social, economic, and environmental aspects that enable the advancement of communities. For this reason, this paper aims to analyze the quality of the energy service in the Non-Interconnected Zones (NIZ) of Colombia. For this purpose, clustering techniques (K-means, K-medoids, divisive analysis clustering, and heatmaps) are applied for data analysis in the context of the NIZ to identify patterns or hidden information in the Colombian government data related to the state of the electricity service in these localities during the years 2019–2020. A descriptive statistical analysis and validation of the results of the clustering techniques is also carried out using R software. Through the implementation of clustering algorithms such as K-means, K-medoids, and divisive analysis clustering, potential areas for the development of renewable and alternative energy projects are identified, considering places with deficiencies in their current electricity service, higher consumption, or places with very low daily hours of electricity service. Additionally, relationships were identified in the dataset that can be considered as tools that would support decision-making for academia and industry, as well as the definition of guidelines or strategies from the government to improve energy efficiency and quality for these places, and consequently, the living conditions of the residents of Colombia’s NIZs.La energía determina los aspectos sociales, económicos y ambientales que permiten el progreso de las comunidades. Por esta razón, este trabajo tiene como objetivo analizar la calidad del servicio de energía en las Zonas No Interconectadas (ZNI) de Colombia. Para ello, se aplican técnicas de clustering (K-means, K-medoids, clustering de análisis divisivo y mapas de calor) para el análisis de datos en el contexto de las ZNI con el fin de identificar patrones o información oculta en los datos del gobierno colombiano relacionados con el estado del servicio de energía eléctrica en estas localidades durante los años 2019-2020. También se realiza un análisis estadístico descriptivo y validación de los resultados de las técnicas de clustering utilizando el software R. A través de la implementación de algoritmos de clustering como K-means, K-medoids, y clustering de análisis divisivo, se identifican áreas potenciales para el desarrollo de proyectos de energías renovables y alternativas, considerando lugares con deficiencias en su servicio eléctrico actual, mayor consumo, o lugares con muy bajas horas diarias de servicio eléctrico. Adicionalmente, se identificaron relaciones en el conjunto de datos que pueden ser consideradas como herramientas que apoyarían la toma de decisiones para la academia y la industria, así como la definición de lineamientos o estrategias desde el gobierno para mejorar la eficiencia y calidad energética de estos lugares, y en consecuencia, las condiciones de vida de los habitantes de las ZNI de Colombia.https://scienti.colciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000192503https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001087002https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0002043532https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000436798https://orcid.org/0000-0003-1166-1982https://orcid.org/0000-0002-2880-9245https://orcid.org/0000-0001-9823-9607https://orcid.org/0000-0003-3701-6340https://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000005961ramon.colmenaresq@campusucc.edu.cogina.maestre@campusucc.edu.comarieth.baquero@campusucc.edu.cokimestansfield@gmail.comjuan.colmenaresq@campusucc.edu.cohttps://scholar.google.com/citations?user=9HLAZYUAAAAJ&hl=eshttps://scholar.google.com/citations?user=-EfDLGsAAAAJ&hl=es&oi=aohttps://scholar.google.com/citations?user=jcAAFWAAAAAJ&hl=es1 - 16Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería Civil, Medellín y EnvigadoIngeniería CivilMedellínhttps://www.mdpi.com/1996-1073/15/20/7644EnergiesColmenares-Quintero, R.F.; Latorre-Noguera, L.F.; Rojas, N.; Kolmsee, K.; Stansfield, K.E.; Colmenares-Quintero, J.C. Computa tional Framework for the Selection of Energy Solutions in Indigenous Communities in Colombia: Kanalitojo Case Study. Cogent Eng. 2021, 8, 1926406.Ochoa, L.L.; Paredes, K.R.; Tejada, J.E. Estudio Comparativo de Técnicas no Supervisadas de Minería de Datos para Segmentación de Alumnos. In Global Partnerships for Development and Engineering Education, Proceedings of the 15th LACCEI International Multi-Conference for Engineering, Education and Technology, Boca Raton, FL, USA, 19–21 July 2017; Latin American and Caribbean Consortium of Engineering Institutions: Boca Raton, FL, USA, 2017; p. 115. ISBN 978-0-9993443-0-9.Razak, M.A.; Yakub, F.; Sulaiman, N.N.I.; Rashid, A.M.Z.; Shaikh Salim, S.A.Z.; Rasid, A.Z.; Abu, A. Energy Consumption Clustering Analysis in Residential Building. In Proceedings of the Intelligent Manufacturing and Mechatronics; Jamaludin, Z., Ali Mokhtar, M.N., Eds.; Springer: Singapore, 2020; pp. 436–450. [CrossRef]Saxena, A.; Prasad, M.; Gupta, A.; Bharill, N.; Patel, O.P.; Tiwari, A.; Er, M.J.; Ding, W.; Lin, C.-T. A Review of Clustering Techniques and Developments. Neurocomputing 2017, 267, 664–681.Chitra, K.; Maheswari, D. A Comparative Study of Various Clustering Algorithms in Data Mining. Int. J. Comput. Sci. Mob. Comput. 2017, 6, 109–115Amat Rodrigo, J. RPubs-Clustering y Heatmaps: Aprendizaje No Supervisado Con R. Available online: https://rpubs.com/ Joaquin_AR/310338 (accessed on 5 December 2021)Rodriguez, M.Z.; Comin, C.H.; Casanova, D.; Bruno, O.M.; Amancio, D.R.; Costa, L.D.; Rodrigues, F.A. Clustering Algorithms: A Comparative Approach. PLoS ONE 2019, 14, e0210236. [Tizón Galisteo, D. Big Data Clustering. Master’s Thesis, UNED, Madrid, Spain, 2017.Gostkowski, M.; Rokicki, T.; Ochnio, L.; Koszela, G.; Wojtczuk, K.; Ratajczak, M.; Szczepaniuk, H.; Bórawski, P.; Bełdycka Bórawska, A. Clustering Analysis of Energy Consumption in the Countries of the Visegrad Group. 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