Prediction of unregistered power consumption lawsuits and its correlated factors based on customer data using extreme gradient boosting model

• Main Authors: Oliveira, Francisco Y., Cutrim, Pedro T., Diniz, Joao O. B., Silva, Giovanni L. F., Quintanilha, Darlan B. P., Neto, Otilio Silva, Fernandes, Vandecia R. M., Junior, Geraldo Braz, Cavalcante, Andre B., Silva, Aristofanes C., Alves, Erika W. B. A. L., de Oliveira, Milton S. L., Pinheiro, Lucas P. A.
• Format: Conference Proceeding
• Language: English
• Subjects: Boosting; Companies; Feature extraction; History; Law; Power demand
• ISSN: 2577-1655
• DOI: 10.1109/SMC.2019.8914366

The great number of lawsuits against energy companies has highlighted the difficult problem of identifying and eliminating failures of services in the energy sector. This work proposes a methodology to predict the issue of new lawsuits in the energy sector on a client database and the identification of factors correlated factors. The methodology is basically divided into 4 stages: (a) data acquisition; (b) feature engineering; (c) feature selection; and (d) classification. The method was performed in a database with more than fifty thousand consumers and shows to be robust in the task of identify the unregistered power consumption lawsuits prediction by achieved an accuracy of 93.89; specificity of 95.58; sensitivity of 88.84; and precision of 87.09. Thus, we demonstrate the feasibility of using XGBoost to solve the problem of unregistered power consumption lawsuits prediction.