Data-driven prediction for the number of distribution network users experiencing typhoon power outages

Typhoons have substantial impacts on power systems and may result in major power outages for distribution network users. Developing prediction models for the number of users going through typhoon power outages is a high priority to support restoration planning. This study proposes a data-driven mode...

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Veröffentlicht in:	IET generation, transmission & distribution Jg. 14; H. 24; S. 5844 - 5850
Hauptverfasser:	Hou, Hui, Yu, Jufang, Geng, Hao, Zhu, Ling, Li, Min, Huang, Yong, Li, Xianqiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 18.12.2020
Schlagworte:	classification data‐driven model data‐driven prediction decision trees disasters distribution network users distribution networks geographical factors gradient boosting decision tree gradient methods linear regression machine learning regression algorithm meteorological factors pattern classification power engineering computing power grid factors power grids power system planning power system reliability power systems random forest random forests regression analysis regression tree restoration planning Special Issue: Advanced Data-Analytics for Power System Operation, Control and Enhanced Situational Awareness storms support vector machines support vector regression typhoon power outages regression tree distribution networks regression analysis linear regression random forests meteorological factors random forest power system reliability typhoon power outages power grids gradient methods geographical factors power grid factors power system planning pattern classification support vector machines distribution network users machine learning regression algorithm disasters data-driven model restoration planning classification power engineering computing gradient boosting decision tree support vector regression decision trees power systems storms data-driven prediction
ISSN:	1751-8687, 1751-8695
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Zusammenfassung:	Typhoons have substantial impacts on power systems and may result in major power outages for distribution network users. Developing prediction models for the number of users going through typhoon power outages is a high priority to support restoration planning. This study proposes a data-driven model to predict the number of distribution network users that may experience power outages when a typhoon passes by. To improve the accuracy of the prediction model, twenty six explanatory variables from meteorological factors, geographical factors and power grid factors are considered. In addition, the authors compared the application effect of five different machine learning regression algorithms, including linear regression, support vector regression, classification and regression tree, gradient boosting decision tree and random forest (RF). It turns out that the RF algorithm shows the best performance. The simulation indicates that the accuracy of the optimal model error within ±30% can reach up to 86%. The proposed method can improve the prediction accuracy through continuous learning on the existing basis. The prediction results can provide efficient guidance for emergency preparedness during typhoon disaster, and can be used as a basis to notify the distribution network users who are likely to lose power.
ISSN:	1751-8687 1751-8695
DOI:	10.1049/iet-gtd.2020.0834