@article {10.3844/ajeassp.2017.32.42,
article_type = {journal},
title = {Evaluation of Customer Behaviour Irregularities in Cameroon Electricity Network using Support Vector Machine},
author = {Bernard, Lekini Nkodo Claude and Benoît, Ndzana and Hamandjoda, Oumarou},
volume = {10},
number = {1},
year = {2017},
month = {Jan},
pages = {32-42},
doi = {10.3844/ajeassp.2017.32.42},
url = {https://thescipub.com/abstract/ajeassp.2017.32.42},
abstract = {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.},
journal = {American Journal of Engineering and Applied Sciences},
publisher = {Science Publications}
}