A new electricity theft detection method using hybrid adaptive sampling and pipeline machine learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17730-7.pdf
Reference64 articles.
1. Ahmad T, Chen H, Wang J et al (2018) Review of various modeling techniques for the detection of electricity theft in smart grid environment. Renew Sust Energ Rev 82:2916–2933
2. Anwar S, Sheltami T, Shakshuki E et al (2019) A framework for single and multiple anomalies localization in pipelines. J Ambient Intell Humaniz Comput 10:2563–2575
3. Arivudainambi D, KA VK, Visu P, et al (2019) Malware traffic classification using principal component analysis and artificial neural network for extreme surveillance. Comput Commun 147:50–57
4. Ayub N, Ali U, Mustafa K et al (2022) Predictive data analytics for electricity fraud detection using tuned cnn ensembler in smart grid. Forecasting 4(4):936–948
5. Azzouguer D, Sebaa A, Hadjout D et al (2023) Fraud detection of electricity consumption using robust exponential and holt-winters smoothing method. In: 2023 IEEE International conference on advanced systems and emergent technologies (IC_ASET), IEEE, pp 01–06
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