Ramos, Caio C. O. [UNESP]Souza, André N. [UNESP]Papa, João P.Falcão, Alexandre X.2014-05-272014-05-272009-12-092009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09.http://hdl.handle.net/11449/71478Fraud 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. © 2009 IEEE.engNon-technical lossesOptimum-path forestArtificial Neural NetworkComputational burdenElectric power companyEnergy systemsForest classifiersFraud detectionNon-technical lossSupervised pattern recognitionClassifiersElectric lossesElectric utilitiesIntelligent systemsPattern recognitionSupport vector machinesNeural networksTrabalho apresentado em evento10.1109/ISAP.2009.5352910Acesso aberto2-s2.0-765490907858212775960494686