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Comparison between ANN and random forest for leakage current alarm prediction
| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Akihiro Yokoyama Nobuyuki Yamaguchi |
| 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. |
| Related Links | http://www.sciencedirect.com/science/article/pii/S235248472031698X |
| e-ISSN | 23524847 |
| DOI | 10.1016/j.egyr.2020.11.271 |
| Journal | Energy Reports |
| Volume Number | 6 |
| Language | English |
| Publisher | Elsevier |
| Publisher Date | 2020-01-01 |
| Publisher Place | United Kingdom |
| Access Restriction | Open |
| Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Electric Security Insulation Monitoring Standardization Dummy Variable |
| Content Type | Text |
| Resource Type | Article |
