| Title |
AI-Based Detection of Power Consumption Behavior of People in a Smart City |
| ID_Doc |
41354 |
| Authors |
Yang, DM; Zhang, YY; He, HM |
| Title |
AI-Based Detection of Power Consumption Behavior of People in a Smart City |
| Year |
2023 |
| Published |
Journal Of Testing And Evaluation, 51, 3 |
| DOI |
10.1520/JTE20220095 |
| Abstract |
In the past, the power consumption behavior of customers was not considered, so the research of power consumption behavior based on artificial intelligence technology was put forward. Combined with artificial intelligence technology, the structure block diagram of electricity consumption analysis is constructed to complete multivariable and multidimensional data storage and management. The initial cluster center is randomly selected from the sample data set to determine the distance between the sample point and the cluster center. Based on the reasonable evaluation index of electric power behavior, the analysis model of electric power consumption behavior is established. The data set is divided by similarity measure, the fuzzy weighted index is initialized, the membership matrix is initialized and updated, the objective function is calculated, and the types of data objects are determined according to the calculation results. Based on this, the user screening process for abnormal power consumption is designed. The k-means clustering algorithm is used to design the diagnostic analysis process, and the closed-loop diagnostic mechanism for abnormal electricity consumption such as stealing and leakage is studied. Experimental results show that the proposed method can effectively distinguish the normal operation mode from the abnormal operation mode of power consumers. |
| Author Keywords |
artificial intelligence technology; power customer; power consumption behavior; evaluation index; initial cluster center |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000883101300001 |
| WoS Category |
Materials Science, Characterization & Testing |
| Research Area |
Materials Science |
| PDF |
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