Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis Technique
With the popularization of smart meters around the world and the appearance of a large amount of electricity consumption data, the analysis of smart meter data is of major interest to electricity distributors around the world. Therefore, we proposed a hybrid artificial intelligence (AI) technique co...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/6225510 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850175273813671936 |
|---|---|
| author | Camille Franklin Mbey Jacques Bikai Felix Ghislain Yem Souhe Vinny Junior Foba Kakeu Alexandre Teplaira Boum |
| author_facet | Camille Franklin Mbey Jacques Bikai Felix Ghislain Yem Souhe Vinny Junior Foba Kakeu Alexandre Teplaira Boum |
| author_sort | Camille Franklin Mbey |
| collection | DOAJ |
| description | With the popularization of smart meters around the world and the appearance of a large amount of electricity consumption data, the analysis of smart meter data is of major interest to electricity distributors around the world. Therefore, we proposed a hybrid artificial intelligence (AI) technique considering sudden changes of consumption in order to accurately predict fraudulent consumers in the smart network. Thus, the proposed hybrid model is based on the support vector machine (SVM) and a particle swarm optimization (PSO) algorithm to detect energy fraudsters in the network. In addition, a real smart grid dataset is used to verify the effectiveness of the proposed algorithm. Moreover, a smart calendar context is modeled showing the scheduling of energy consumption. The effectiveness of the proposed technique is evaluated using performance coefficients such as precision, recall, F1-score, and area under ROC curve (AUC). We also perform sensitivity analysis through regression, variance, and variogram analysis. The results obtained give a performance of 98.9% in the detection of irregular consumers in the smart power grid. These results demonstrate the effectiveness of the proposed method compared to that in the literature. |
| format | Article |
| id | doaj-art-ce767bd70138430e9e301db88fbf6aea |
| institution | OA Journals |
| issn | 2090-0155 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-ce767bd70138430e9e301db88fbf6aea2025-08-20T02:19:30ZengWileyJournal of Electrical and Computer Engineering2090-01552024-01-01202410.1155/2024/6225510Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis TechniqueCamille Franklin Mbey0Jacques Bikai1Felix Ghislain Yem Souhe2Vinny Junior Foba Kakeu3Alexandre Teplaira Boum4Department of Electrical Engineering, ENSETDepartment of Energy EngineeringDepartment of Electrical Engineering, ENSETDepartment of Electrical Engineering, ENSETDepartment of Electrical Engineering, ENSETWith the popularization of smart meters around the world and the appearance of a large amount of electricity consumption data, the analysis of smart meter data is of major interest to electricity distributors around the world. Therefore, we proposed a hybrid artificial intelligence (AI) technique considering sudden changes of consumption in order to accurately predict fraudulent consumers in the smart network. Thus, the proposed hybrid model is based on the support vector machine (SVM) and a particle swarm optimization (PSO) algorithm to detect energy fraudsters in the network. In addition, a real smart grid dataset is used to verify the effectiveness of the proposed algorithm. Moreover, a smart calendar context is modeled showing the scheduling of energy consumption. The effectiveness of the proposed technique is evaluated using performance coefficients such as precision, recall, F1-score, and area under ROC curve (AUC). We also perform sensitivity analysis through regression, variance, and variogram analysis. The results obtained give a performance of 98.9% in the detection of irregular consumers in the smart power grid. These results demonstrate the effectiveness of the proposed method compared to that in the literature.http://dx.doi.org/10.1155/2024/6225510 |
| spellingShingle | Camille Franklin Mbey Jacques Bikai Felix Ghislain Yem Souhe Vinny Junior Foba Kakeu Alexandre Teplaira Boum Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis Technique Journal of Electrical and Computer Engineering |
| title | Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis Technique |
| title_full | Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis Technique |
| title_fullStr | Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis Technique |
| title_full_unstemmed | Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis Technique |
| title_short | Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis Technique |
| title_sort | electricity theft detection in a smart grid using hybrid deep learning based data analysis technique |
| url | http://dx.doi.org/10.1155/2024/6225510 |
| work_keys_str_mv | AT camillefranklinmbey electricitytheftdetectioninasmartgridusinghybriddeeplearningbaseddataanalysistechnique AT jacquesbikai electricitytheftdetectioninasmartgridusinghybriddeeplearningbaseddataanalysistechnique AT felixghislainyemsouhe electricitytheftdetectioninasmartgridusinghybriddeeplearningbaseddataanalysistechnique AT vinnyjuniorfobakakeu electricitytheftdetectioninasmartgridusinghybriddeeplearningbaseddataanalysistechnique AT alexandreteplairaboum electricitytheftdetectioninasmartgridusinghybriddeeplearningbaseddataanalysistechnique |