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- W3197277564 abstract "In order to solve the problem of group renting in the city and eliminate the hidden safety hazards caused by which, we take use of the isolated forest model to filter out non -group renting users from the general electricity customers in the first step. So in this case, a semi-supervised learning problem is transformed into a supervised classification and identification problem. Then a random forest model is built to train the classifier which can discriminate the electricity consumption data of group renters from the non-group renting ones. Finally, the electricity consumption data of the Yuhuatai District of Nanjing City are employed in the experiment of this paper, and the classification accuracy on the 99-household test set reached 99.89%, which shows the effectiveness of the proposed classification model and method in this paper." @default.
- W3197277564 created "2021-09-13" @default.
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- W3197277564 date "2020-11-01" @default.
- W3197277564 modified "2023-09-23" @default.
- W3197277564 title "Power consumption behavior detection of group renters based on random forest" @default.
- W3197277564 cites W2097874565 @default.
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- W3197277564 doi "https://doi.org/10.1109/icris52159.2020.00150" @default.
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