Data Analysis of Electricity Service in Colombia’s Non-Interconnected Zones through Different Clustering Techniques

Ramón Fernando Colmenares-Quintero (), Gina Maestre-Gongora, Marieth Baquero-Almazo, Kim E. Stansfield and Juan Carlos Colmenares-Quintero
Additional contact information
Ramón Fernando Colmenares-Quintero: Faculty of Engineering, Universidad Cooperativa de Colombia, Calle 50A No. 41-34, Medellín 050012, Colombia
Gina Maestre-Gongora: Faculty of Engineering, Universidad Cooperativa de Colombia, Calle 50A No. 41-34, Medellín 050012, Colombia
Marieth Baquero-Almazo: Faculty of Engineering, Universidad Cooperativa de Colombia, Calle 50A No. 41-34, Medellín 050012, Colombia
Kim E. Stansfield: VOCATE Ltd., 2 Fountain Place, Worcester WR1 3HW, UK
Juan Carlos Colmenares-Quintero: Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland

Energies, 2022, vol. 15, issue 20, 1-16

Abstract: 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.

Keywords: data mining; clustering; partitioning clusters; hierarchical clusters; energy service (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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