부산대학교 도서관

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.