Artificial Intelligence-Assisted Methodology for Dataset Reduction Applied to the Establishment of Power Interruption Limits in Brazil

Rhafael Freitas da Costa, Gabriela Rosalee Weigert-Dalagnol, Débora Cintia Marcilio (), Lúcio de Medeiros, Eunelson José da Sila Junior, Xie Jiayu, Elías Pablo Curi, Sonia Magdalena Juan, Rafael Taranto Polizel and Herber Fontoura
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Rhafael Freitas da Costa: Institute of Technology for Development—Lactec, Curitiba 80215-090, Brazil
Gabriela Rosalee Weigert-Dalagnol: Institute of Technology for Development—Lactec, Curitiba 80215-090, Brazil
Débora Cintia Marcilio: Institute of Technology for Development—Lactec, Curitiba 80215-090, Brazil
Lúcio de Medeiros: Institute of Technology for Development—Lactec, Curitiba 80215-090, Brazil
Eunelson José da Sila Junior: Institute of Technology for Development—Lactec, Curitiba 80215-090, Brazil
Xie Jiayu: Institute of Technology for Development—Lactec, Curitiba 80215-090, Brazil
Elías Pablo Curi: Quantum Brazil Ltd., Urca, Córdoba CP 5009, Argentina
Sonia Magdalena Juan: Quantum Brazil Ltd., Urca, Córdoba CP 5009, Argentina
Rafael Taranto Polizel: CPFL Energia, Campinas 13088-900, Brazil
Herber Fontoura: CPFL Energia, Campinas 13088-900, Brazil

Energies, 2023, vol. 16, issue 19, 1-13

Abstract: Definitions of methodologies to regulate the quality of electricity supply services are a topic under active discussion in Brazil and worldwide. There are various ways to define limits and quality service goals. In Brazil, the regulation of limit indicators for consumer unit sets is carried out by the National Electric Energy Agency. Its latest revision took place in 2014, under the framework of Public Announcement No. 29/2014. The primary contribution of this research is the proposition of an artificial intelligence-assisted methodology, specifically utilizing machine-learning techniques capable of organizing and selecting the most relevant attributes for representing similar consumer sets. Tests were conducted with real data from the 2020 system. The results demonstrated that this methodology can select attributes from different categories, achieving data representativeness and clustering scores superior to those attained with attributes selected by the current ANEEL methodology. Furthermore, the proposed methodology exhibits greater replicability compared to the current approach. These outcomes contribute to the modernization of quality regulation in the electricity distribution sector, benefiting all stakeholders in the industry.

Keywords: machine learning; clustering; regulatory methodologies for quality (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: 2023
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