Evaluation of Customer Behaviour Irregularities in Cameroon Electricity Network using Support Vector Machine
- 1 University of Yaounde I, Cameroon
Copyright: © 2020 Lekini Nkodo Claude Bernard, Ndzana Benoît and Oumarou Hamandjoda. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Non-Technical Losses (NTLs) in the Cameroonians electricity network are approximately 30 to 40% of production and are estimated at several billion CFA francs per year for National Electricity Company (ENEO); Hence the importance of finding effective solutions to fight against these losses. The purpose of this work was to develop a tool for the fraud detection for Cameroon National Electricity Company (ENEO) using support vector machines which consisted in data preprocessing base on the load profile, development of a model for classification, parameter optimization and detection of customers irregularities and prediction.
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- Fraud Detection
- Support Vector Machine
- Load Profile