Comparison between ANN and random forest for leakage current alarm prediction. (December 2020)

• Record Type: Journal Article
• Authors: Yokoyama, Akihiro; Yamaguchi, Nobuyuki
• Is Part Of: Energy reports. Volume 6(2020)Supplement 9
• Page Start: 150
• Page End: 157
• Publication Date: 2020-12
• Subjects: Electric security -- Insulation monitoring -- Standardization -- Dummy variable; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2020.11.271
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: In order to improve the efficiency of the electrical safety operations of private electric facilities, the use of AI and IoT is expected. In this paper, we propose a leakage current alarm prediction model using a random forest and an artificial neural network. Customer information, periodic inspection history, alarm occasions on the previous day, and weather information are used as explanatory variables. A grid search was performed for hyperparameter optimization of each model, and generalization performance was evaluated using OOB verification and cross-validation. As a result of comparing the performances of the two models by the PR curve, it was found that the random forest had a larger PR curve and had better prediction performance.