Comparing different labeling strategies in anomalous power consumptions detection

Abstract

Detecting anomalous events is a complex task, specially when it should be performed manually and for several hours. In the case of electrical power consumptions, the detection of non-technical losses also has a high economic impact. The diversity and big number of consumption records, makes it very important to find an efficient automatic method for detecting the largest number of frauds. This work analyses the performance of a strategy based on learning from expert labeling: suspect/no-suspect, with one using inspection labels: fraud/no-fraud. Results show that the proposed framework, suitable for imbalance problems, improves performance in terms of the Fmeasure with inspection labels, avoiding hours of experts labeling.