E3S Web Conf.
Volume 152, 20202019 International Conference on Power, Energy and Electrical Engineering (PEEE 2019)
|Number of page(s)||5|
|Section||Power Electronics and Transmission Technology|
|Published online||14 February 2020|
Optimal hyperparameters for random forest to predict leakage current alarm on premises
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: email@example.com
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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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