Prediction of user outage under typhoon disaster based on multi-algorithm Stacking integration
•Variable data extraction and prediction were carried out with 1 km × 1 km grid as unit.•Using Stacking to integrate multiple algorithms to achieve outage prediction.•ArcGIS visualizes the results and displays the grid distribution conveniently. Prediction of user outage under typhoon disaster is of...
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| Veröffentlicht in: | International journal of electrical power & energy systems Jg. 131; S. 107123 |
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| Sprache: | Englisch |
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01.10.2021
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| ISSN: | 0142-0615, 1879-3517 |
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| Abstract | •Variable data extraction and prediction were carried out with 1 km × 1 km grid as unit.•Using Stacking to integrate multiple algorithms to achieve outage prediction.•ArcGIS visualizes the results and displays the grid distribution conveniently.
Prediction of user outage under typhoon disaster is of great significance for power grid disaster prevention and mitigation. Based on the idea of Stacking integration in machine learning, this paper constructs a forecasting model of user outage under typhoon disaster. It includes base learner layer and meta learner layer. In the base learner layer, random forest, adaptive boosting, extremely tree, gradient boosting decision tree, support vector machine and logistic regression are selected. Then the XGBoost algorithm is selected in the meta learner layer. Taking one of the most frequently struck area Xuwen County of Guangdong, China as the research object, the model is verified by typhoon “Rammasun(2014)”,“Kalmaegi(2014)” and “Mujigae(2015)”. The results show that the accuracy and recall of the prediction model based on multi-algorithm Stacking integration can reach 0.7678 and 0.9059 respectively. It can well realize the prediction of user outage under typhoon disaster. |
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| AbstractList | •Variable data extraction and prediction were carried out with 1 km × 1 km grid as unit.•Using Stacking to integrate multiple algorithms to achieve outage prediction.•ArcGIS visualizes the results and displays the grid distribution conveniently.
Prediction of user outage under typhoon disaster is of great significance for power grid disaster prevention and mitigation. Based on the idea of Stacking integration in machine learning, this paper constructs a forecasting model of user outage under typhoon disaster. It includes base learner layer and meta learner layer. In the base learner layer, random forest, adaptive boosting, extremely tree, gradient boosting decision tree, support vector machine and logistic regression are selected. Then the XGBoost algorithm is selected in the meta learner layer. Taking one of the most frequently struck area Xuwen County of Guangdong, China as the research object, the model is verified by typhoon “Rammasun(2014)”,“Kalmaegi(2014)” and “Mujigae(2015)”. The results show that the accuracy and recall of the prediction model based on multi-algorithm Stacking integration can reach 0.7678 and 0.9059 respectively. It can well realize the prediction of user outage under typhoon disaster. |
| ArticleNumber | 107123 |
| Author | Hou, Hui Chen, Xi Li, Min Huang, Yong Yu, Jufang Zhu, Ling |
| Author_xml | – sequence: 1 givenname: Hui surname: Hou fullname: Hou, Hui email: houhui@whut.edu.cn organization: School of Automation, Wuhan University of Technology, Wuhan 430070, China – sequence: 2 givenname: Xi surname: Chen fullname: Chen, Xi organization: School of Automation, Wuhan University of Technology, Wuhan 430070, China – sequence: 3 givenname: Min surname: Li fullname: Li, Min organization: Guangdong Power Grid Co., Ltd., Guangzhou 510080, China – sequence: 4 givenname: Ling surname: Zhu fullname: Zhu, Ling organization: Guangdong Power Grid Co., Ltd., Guangzhou 510080, China – sequence: 5 givenname: Yong surname: Huang fullname: Huang, Yong organization: Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510080, China – sequence: 6 givenname: Jufang surname: Yu fullname: Yu, Jufang organization: School of Automation, Wuhan University of Technology, Wuhan 430070, China |
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| Cites_doi | 10.1109/TPWRD.2002.804006 10.1016/j.procs.2020.06.071 10.1109/AEEES48850.2020.9121536 10.1109/ACCESS.2019.2916382 10.1007/s11069-010-9672-9 10.1109/ACCESS.2018.2877078 10.1111/j.1539-6924.2009.01280.x 10.1016/j.neucom.2016.07.036 10.1109/TPWRS.2015.2429656 10.1016/j.ress.2011.10.012 10.1016/j.gecco.2019.e00856 10.1016/j.jclepro.2018.08.207 10.1016/j.ijepes.2019.105711 10.1016/j.ress.2007.03.038 10.1061/(ASCE)1076-0342(2005)11:4(258) 10.1023/A:1010933404324 10.1109/ACCESS.2014.2365716 10.1007/s11069-015-1908-2 |
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| Title | Prediction of user outage under typhoon disaster based on multi-algorithm Stacking integration |
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