Anomaly Detection in Smart Meter Data for Preventing Potential Smart Grid Imbalance

Author:

Jaiswal Rituka1,Maatug Fadwa1,Davidrajuh Reggie1,Rong Chunming1

Affiliation:

1. University Of Stavanger, Norway

Publisher

ACM

Reference33 articles.

1. IoT-enabled smart grid via SM: An overview

2. Alberto Fernández , Salvador García , Mikel Galar , Ronaldo C. Prati , Bartosz Krawczyk , and Francisco Herrera . 2018. Learning From Imbalanced Data Sets . Springer , Cham, Switzerland . http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN= 1920 612&scope=site Alberto Fernández, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk, and Francisco Herrera. 2018. Learning From Imbalanced Data Sets. Springer, Cham, Switzerland. http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1920612&scope=site

3. Anastasios Bellas , Charles Bouveyron , Marie Cottrell , and Jerome Lacaille . 2014. Anomaly Detection Based on Confidence Intervals Using SOM with an Application to Health Monitoring. arXiv:1508.04154 [stat] 295 ( 2014 ), 145–155. https://doi.org/10.1007/978-3-319-07695-9_14 arXiv:1508.04154. Anastasios Bellas, Charles Bouveyron, Marie Cottrell, and Jerome Lacaille. 2014. Anomaly Detection Based on Confidence Intervals Using SOM with an Application to Health Monitoring. arXiv:1508.04154 [stat] 295 (2014), 145–155. https://doi.org/10.1007/978-3-319-07695-9_14 arXiv:1508.04154.

4. Jason Brownlee. 2021. Imbalanced Classification with Python(v1.3 ed.). Jason Brownlee. https://machinelearningmastery.com/imbalanced-classification-with-python/ Jason Brownlee. 2021. Imbalanced Classification with Python(v1.3 ed.). Jason Brownlee. https://machinelearningmastery.com/imbalanced-classification-with-python/

5. Lars Buitinck , Gilles Louppe , Mathieu Blondel , Fabian Pedregosa , Andreas Mueller , Olivier Grisel , Vlad Niculae , Peter Prettenhofer , Alexandre Gramfort , Jaques Grobler , Robert Layton , Jake Vanderplas , Arnaud Joly , Brian Holt , and Gaël Varoquaux . 2013. API design for machine learning software: experiences from the scikit-learn project. arXiv:1309.0238 [cs] (Sept . 2013 ). http://arxiv.org/abs/1309.0238 arXiv:1309.0238. Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake Vanderplas, Arnaud Joly, Brian Holt, and Gaël Varoquaux. 2013. API design for machine learning software: experiences from the scikit-learn project. arXiv:1309.0238 [cs] (Sept. 2013). http://arxiv.org/abs/1309.0238 arXiv:1309.0238.

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3. Research on Accurate Audit of Defaulting Power Consumption Based on K-Means Multidimensional Overrun Judgment;2023 IEEE 14th International Conference on Software Engineering and Service Science (ICSESS);2023-10-17

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