engleski

Detection of Suspicious Patterns of Energy Consumption Using Neural Network Trained by Generated Samples

In this paper two different methods for non-technical losses (NTL) detection are analyzed and new approach is proposed, based on the noticed drawbacks. It is shown that NTL can be successfully detected by a neural network trained by “artificial”, i.e., generated samples. This approach eliminates the need for many hard-to-obtain real life samples and the network can easily be trained to detect some new, nontypical occurrences in the system. This makes the proposed solution suitable for large companies that supply many different consumers who possibly change their consumption habits.

non-technical loss; classification; neural network; sampling

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