Fast Non-Technical Losses Identification Through Optimum-Path Forest

Abstract

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as artificial neural networks and support vector machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the optimum-path forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support vector machines, but much faster. Comparisons among these classifiers are also presented.