MENEZES, D. P.; http://lattes.cnpq.br/1822892320151644; MENEZES, Diogo Passos.
Resumo:
The search for measurement points with irregularities in energy distribution systems has
shown itself to be a costly task when accomplished through non-automated processes,
which require great amounts of time and physico-mental effort of those involved. These
costs are increased for low-voltage services, where the collection of energy consumption
readings are field-based and there is no monitoring of several other electrical quantities
through telemetry. This work, then, implements an assistant based on artificial neural
networks theory for detecting irregularities (potential frauds) of monthly energy
measurements. With basis in historical consumption, fraudulent and non-fraudulent
clients are classified with a success rate of up to 83.80%, utilizing a multilayer perceptron
artificial neural network trained with the resilient backpropagation algorithm. The
outcomes presented in this work can be further utilized to approach the fighting of nontechnical
losses caused by fraudulent means, with neural networks in electrical energy
distribution systems.