DESENVOLVIMENTO DE UM SISTEMA PARA CLASSIFICAÇÃO DE ANORMALIDADES NO CONSUMO DE ENERGIA ELÉTRICA

Abstract

This work proposes a computational technique for classification of electricity consumption profiles. The approach is based on the assumption that it s possible to find out groups of consumers with similar patterns of energy use. So, given the found groups, which can be also viewed as a normal consumption profile, ones can associate a high chance of fraud or abnormality to that consumers lying more apart from the groups. The methodology comprises two steps. A fuzzy clustering c-means-based is done in order to search for consumers with similar consumption profiles, in the first one. Afterwards, a fuzzy classification is performed using a fuzzy membership matrix and the Euclidian distance to the cluster centers. Then, the distance measures are normalized and ordered, yielding an unitary index score, where the possible fraudulent or abnormal consumers are those with the higher scores. The approach was tested and validated with real data base, showing good performance in both fraud and metering defect detection tasks.