| Issue |
E3S Web Conf.
Volume 152, 2020
2019 International Conference on Power, Energy and Electrical Engineering (PEEE 2019)
|
|
|---|---|---|
| Article Number | 03003 | |
| Number of page(s) | 5 | |
| Section | Power Electronics and Transmission Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202015203003 | |
| Published online | 14 February 2020 | |
Optimal hyperparameters for random forest to predict leakage current alarm on premises
1
Tokyo University of Science, Department of Engineering, 125-8585 Tokyo, Japan
2
Tokyo University of Science, Graduate School of Engineering, 125-8585 Tokyo, Japan
* Corresponding author: n-yama@rs.tus.ac.jp
While the number of private electrical facilities is increasing, there are not enough security personnel to perform the security work. In this paper, we propose a random forest model for predicting leakage current alarms in order to improve the efficiency of electrical safety operations. A random forest was created using periodic inspection data, alarm data, and meteorological data as explanatory variables, and generalization performance was evaluated by OOB-based F-measure. In order to obtain the highest performance, a grid search was performed to optimize the hyperparameters. As a result, it was possible to achieve alarm prediction with a certain level of performance. In addition, the optimal hyperparameters were found by grid search, and the F-measure was improved.
© The Authors, published by EDP Sciences, 2020
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