%0 Conference Paper %1 9289669 %A Olivares-Rojas, Juan C. %A Reyes-Archundia, Enrique %A Rodriíguez-Maya, Noel E. %A Gutiérrez-Gnecchi, José A. %A Molina-Moreno, Ismael %A Cerda-Jacobo, Jaime %B 2020 IEEE International Conference on Engineering Veracruz (ICEV) %D 2020 %K detection energy learning machine model myown theft %P 1-6 %R 10.1109/ICEV50249.2020.9289669 %T Machine Learning Model for the Detection of Electric Energy Fraud using an Edge-Fog Computing Architecture %U https://ieeexplore.ieee.org/document/9289669/ %X One of the most relevant applications today in the smart grid is the use of artificial intelligence and analysis techniques over the data generated, allowing both end-users and utility companies new applications. Energy fraud is considered one of the main non-technical losses for utilities that translate into economic losses, so being able to detect and minimize fraud becomes a vital necessity for utilities. This paper presents a machine learning model that allows the detection of abnormal behaviors in readings of consumption and/or production of electrical energy that can be classified as energy fraud. The proposed model is based on regression and classification techniques over an edge-fog computing architecture, and the results obtained show that its implementation in smart metering systems is adequate.

@inproceedings{9289669, abstract = {One of the most relevant applications today in the smart grid is the use of artificial intelligence and analysis techniques over the data generated, allowing both end-users and utility companies new applications. Energy fraud is considered one of the main non-technical losses for utilities that translate into economic losses, so being able to detect and minimize fraud becomes a vital necessity for utilities. This paper presents a machine learning model that allows the detection of abnormal behaviors in readings of consumption and/or production of electrical energy that can be classified as energy fraud. The proposed model is based on regression and classification techniques over an edge-fog computing architecture, and the results obtained show that its implementation in smart metering systems is adequate.}, added-at = {2021-06-04T16:26:22.000+0200}, author = {Olivares-Rojas, Juan C. and Reyes-Archundia, Enrique and Rodriíguez-Maya, Noel E. and Gutiérrez-Gnecchi, José A. and Molina-Moreno, Ismael and Cerda-Jacobo, Jaime}, biburl = {https://www.bibsonomy.org/bibtex/280f0a12d401811ea42052faab4329107/jcolivares}, booktitle = {2020 IEEE International Conference on Engineering Veracruz (ICEV)}, doi = {10.1109/ICEV50249.2020.9289669}, interhash = {87ed8e7f8ed93e6616de7d3e33ed37e9}, intrahash = {80f0a12d401811ea42052faab4329107}, keywords = {detection energy learning machine model myown theft}, month = oct, pages = {1-6}, timestamp = {2021-06-04T16:26:22.000+0200}, title = {Machine Learning Model for the Detection of Electric Energy Fraud using an Edge-Fog Computing Architecture}, url = {https://ieeexplore.ieee.org/document/9289669/}, year = 2020 }