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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Vydáno v:International journal of electrical power & energy systems Ročník 131; s. 107123
Hlavní autoři: Hou, Hui, Chen, Xi, Li, Min, Zhu, Ling, Huang, Yong, Yu, Jufang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.10.2021
Témata:
Outage prediction
Stacking integration
Typhoon
User outage
Typhoon
Outage prediction
User outage
Stacking integration
ISSN:0142-0615, 1879-3517
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Shrnutí:•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.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2021.107123