Power consumption behavior detection of group renters based on random forest

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 pr...

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Bibliographic Details
Published in2020 International Conference on Robots & Intelligent System (ICRIS) pp. 594 - 597
Main Authors Cui, Gaoying, Shao, Xuesong, Duan, Meimei, Li, Yue, Xu, Xiaolin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2020
Subjects
Data models
Forestry
Group renting
Isolated forest model
Power demand
Random forest algorithm
Safety management
Semi-supervised problem
Semisupervised learning
Training
Urban areas
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Summary: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.
DOI:10.1109/ICRIS52159.2020.00150