Detection of abnormalities and electricity theft using genetic Support Vector Machines

@article{Nagi2008DetectionOA,
  title={Detection of abnormalities and electricity theft using genetic Support Vector Machines},
  author={J. Nagi and K. S. Yap and S. K. Tiong and S. K. Ahmed and A. M. Mohammad},
  journal={TENCON 2008 - 2008 IEEE Region 10 Conference},
  year={2008},
  pages={1-6}
}
Efficient methods for detecting electricity fraud has been an active research area in recent years. [...] Key Method This hybrid GA-SVM model preselects suspected customers to be inspected onsite for fraud based on abnormal consumption behavior. The proposed approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities.Expand

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References

SHOWING 1-10 OF 12 REFERENCES
Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method
Wavelet based feature extraction and multiple classifiers for electricity fraud detection
Fraud identification in electricity company customers using decision tree
Load profiling and data mining techniques in electricity deregulated market
Allocation of the load profiles to consumers using probabilistic neural networks
Weighted Mahalanobis Distance Kernels for Support Vector Machines
A statistical method to minimize electrical energy losses in a local electricity distribution network
Adaptation in natural and artificial systems
...
1
2
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