Title: A predictive model of electricity quality indicator in distribution subsidiaries

Authors: Ana Flávia Lopes Gonçalves; Rafael de Magalhães Dias Frinhani; Bruno Guazzelli Batista; Rafael Perez Pagan; Edvard Martins de Oliveira; Bruno Tardiole Kuehne; João Paulo Reus Rodrigues Leite; João Víctor de Moraes Santos Gomes

Addresses: Institute of Systems Engineering and Information Technology, Federal University of Itajubá, Itajubá, MG, Brazil ' Mathematics and Computer Sciences Institute, Federal University of Itajubá, Itajubá, MG, Brazil ' Mathematics and Computer Sciences Institute, Federal University of Itajubá, Itajubá, MG, Brazil ' Sinapse DDMX Labs, DDMX – Smart Productivity, Itajubá, MG, Brazil ' Systems Engineering and Information Technology Institute, Federal University of Itajubá, Itajubá, MG, Brazil ' Systems Engineering and Information Technology Institute, Federal University of Itajubá, Itajubá, MG, Brazil ' Systems Engineering and Information Technology Institute, Federal University of Itajubá, Itajubá, MG, Brazil ' Systems Engineering and Information Technology Institute, Federal University of Itajubá, Itajubá, MG, Brazil

Abstract: Electricity concessionaires give off high financial amounts annually in repairs to consumers that experience service unavailability. Availability of the energy supply is a major challenge because the distribution infrastructure is constantly affected by climatic, environmental, and social causes. To assist decision making in mitigating grid failures, this study aims to predict the number of incidences of electricity shortage for consumers. A predictive model was developed using predictive data analysis and conforms to a knowledge discovery process. A hybrid classifier was developed from the model, using both unsupervised and supervised methods. The experiments were carried out with real incidence and climatic data from four subsidiaries of an energy concessionaire. The results show the forecasting model's feasibility, which presented classification accuracy between 58.33% to 91.66%. The results show that peculiarities in terms of geographic location, energy demand, and climatic conditions make it difficult to use a generic prediction model.

Keywords: electric quality indicator; predictive data analysis; machine learning; unsupervised methods; supervised methods; knowledge discovery in data.

DOI: 10.1504/IJBIDM.2022.126498

International Journal of Business Intelligence and Data Mining, 2022 Vol.21 No.4, pp.419 - 442

Received: 22 Mar 2021
Accepted: 26 Jun 2021
Published online: 27 Oct 2022 *

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