Name
Title | ||
|---|---|---|
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids. |
Abstract | ||
|---|---|---|
Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, t... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TII.2017.2785963 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Smart grids,Anomaly detection,Correlation,Support vector machines,Meters,Sensors,Neural networks | Anomaly detection,Smart grid,Computer science,Convolutional neural network,Electricity,Support vector machine,Real-time computing,Artificial neural network | Journal |
Volume | Issue | ISSN |
14 | 4 | 1551-3203 |
Citations | PageRank | References |
14 | 0.69 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zibin Zheng | 1 | 3731 | 199.37 |
| Ya-Tao Yang | 2 | 61 | 7.14 |
| Xiangdong Niu | 3 | 14 | 0.69 |
| Hongning Dai | 4 | 629 | 62.25 |
| Yuren Zhou | 5 | 721 | 49.79 |