Open Access
Issue |
MATEC Web Conf.
Volume 189, 2018
2018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018)
|
|
---|---|---|
Article Number | 03001 | |
Number of page(s) | 7 | |
Section | Cloud & Network | |
DOI | https://doi.org/10.1051/matecconf/201818903001 | |
Published online | 10 August 2018 |
- Dincer I. Energy and GDP analysis of OECD countries. Energy conversion and management, (1997), 38(7): 685-696 [CrossRef] [Google Scholar]
- Smith T B. Electricity theft: a comparative analysis. Energy Policy, (2004), 32(18): 2067-2076 [CrossRef] [Google Scholar]
- Nizar A H, Dong Z Y, Zhao J H, et al. A data mining based NTL analysis method. Power Engineering Society General Meeting, 2007. IEEE. IEEE, (2007): 1-8 [Google Scholar]
- Jiang R, Tagaris H, Lachsz A, et al. Wavelet based feature extraction and multiple classifiers for electricity fraud detection. Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES. IEEE, (2002), 3: 2251-2256 [CrossRef] [Google Scholar]
- Leal A G, Boldt M. A big data analytics design patterns to select customers for electricity theft inspection. Transmission & Distribution Conference and Exposition-Latin America (PES T&D-LA), 2016 IEEE PES. IEEE, (2016): 1-6 [Google Scholar]
- Smith T B. Electricity theft: a comparative analysis. Energy Policy, (2004), 32(18): 2067-2076 [CrossRef] [Google Scholar]
- Yap K S, Tiong S K, Nagi J, et al Comparison of supervised learning techniques for non–technical loss detection in power utility. International Review on Computers and Software, (2012), 7(2)F626-636 [Google Scholar]
- Monedero I, Biscarri F, Leon C, et al. Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees. International Journal of Electrical Power & Energy Systems, (2012), 34(1): 90-98 [CrossRef] [Google Scholar]
- Gontijo E M, Delaiba A C, Mazina E, et al. Fraud identification in electricity company customers using decision tree. Systems, Man and Cybernetics, 2004 IEEE International Conference on. IEEE, (2004), 4: 3730-3734 [Google Scholar]
- Nagi J, Mohammad A M, Yap K S, et al. Non-technical loss analysis for detection of electricity theft using support vector machines. Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International. IEEE, (2008): 907-912 [Google Scholar]
- Nagi J, Yap K S, Tiong S K, et al. Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system. IEEE Transactions on power delivery, (2011), 26(2): 1284-1285. [CrossRef] [Google Scholar]
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