dc.contributor.author | Wang, Jiangzhao | |
dc.contributor.author | Zhu, Yanqing | |
dc.contributor.author | Gao, Yunpeng | |
dc.contributor.author | Cai, Ziwen | |
dc.contributor.author | Sun, Yichuang | |
dc.contributor.author | Peng, Fenghua | |
dc.date.accessioned | 2023-07-05T09:45:02Z | |
dc.date.available | 2023-07-05T09:45:02Z | |
dc.date.issued | 2023-07-03 | |
dc.identifier.citation | Wang , J , Zhu , Y , Gao , Y , Cai , Z , Sun , Y & Peng , F 2023 , ' Detecting Energy Theft in Different Regions Based on Convolutional and Joint Distribution Adaptation ' , IEEE Transactions on Instrumentation and Measurement . https://doi.org/10.1109/TIM.2023.3291769 | |
dc.identifier.issn | 0018-9456 | |
dc.identifier.uri | http://hdl.handle.net/2299/26476 | |
dc.description | © 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TIM.2023.3291769 | |
dc.description.abstract | Electricity theft has been a major concern all over the world. There are great differences in electricity consumption among residents from different regions. However, existing supervised methods of machine learning are not in detecting electricity theft from different regions, while the development of transfer learning provides a new view for solving the problem. Hence, an electricity-theft detection method based on Convolutional and Joint Distribution Adaptation(CJDA) is proposed. In particular, the model consists of three components: convolutional component (Conv), Marginal Distribution Adaptation(MDA) and Conditional Distribution Adaptation(CDA). The convolutional component can efficiently extract the customer’s electricity characteristics. The Marginal Distribution Adaptation can match marginal probability distributions and solve the discrepancies of residents from different regions while Conditional Distribution Adaptation can reduce the difference of the conditional probability distributions and enhance the discrimination of features between energy thieves and normal residents. As a result, the model can find a matrix to adapt the electricity residents in different regions to achieve electricity theft detection. The experiments are conducted on electricity consumption data from the Irish Smart Energy Trial and State Grid Corporation of China and metrics including ACC, Recall, FPR, AUC and F1Score are used for evaluation. Compared with other methods including some machine learning methods such as DT, RF and XGBoost, some deep learning methods such as RNN, CNN and Wide & Deep CNN and some up-to-date methods such as BDA, WBDA, ROCKET and MiniROCKET, our proposed method has a better effect on identifying electricity theft from different regions. | en |
dc.format.extent | 9 | |
dc.format.extent | 3492209 | |
dc.language.iso | eng | |
dc.relation.ispartof | IEEE Transactions on Instrumentation and Measurement | |
dc.title | Detecting Energy Theft in Different Regions Based on Convolutional and Joint Distribution Adaptation | en |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Department of Engineering and Technology | |
dc.contributor.institution | Centre for Engineering Research | |
dc.contributor.institution | Centre for Future Societies Research | |
dc.contributor.institution | Communications and Intelligent Systems | |
dc.description.status | Peer reviewed | |
rioxxterms.versionofrecord | 10.1109/TIM.2023.3291769 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |