Fast non-technical losses identification through Optimum-Path Forest
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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. © 2009 IEEE.
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Non-technical losses, Optimum-path forest, Artificial Neural Network, Computational burden, Electric power company, Energy systems, Forest classifiers, Fraud detection, Non-technical loss, Supervised pattern recognition, Classifiers, Electric losses, Electric utilities, Intelligent systems, Pattern recognition, Support vector machines, Neural networks
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Inglês
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2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09.
