Design of Electricity Theft Monitoring System in Customer Consumptions
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Abstract
This paper proposes a computational technique for the classification of electricity consumption profiles. The methodology is
comprised of two steps. In the first one, a C-means-based fuzzy clustering is performed in order to find consumers with similar consumption
profiles. Afterwards, a fuzzy classification is performed using a fuzzy membership matrix and the Euclidean distance to the cluster centers.
Then, the distance measures are normalized and ordered, yielding a unitary index score, where the potential fraudsters or users with irregular
patterns of consumption have the highest scores. The approach was tested and validated on a real database, showing good performance in tasks
of fraud and measurement defect detection.
Index Terms—Data mining, electricity theft, fuzzy clustering, nontechnical losses.
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