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

Content Provider	IET Digital Library
Author	Hou, Hui Yu, Jufang Geng, Hao Zhu, Ling Li, Min Huang, Yong Li, Xianqiang
Abstract	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.
Starting Page	5844
Ending Page	5850
Page Count	7
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 24, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/24
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2020.0834
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-08-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Classification Combinatorial Mathematics Data Handling Technique Data-driven Model Data-driven Prediction Decision Tree Disasters Distribution Network Distribution Network User Geographical Factors Gradient Boosting Decision Tree Gradient Method Interpolation And Function Approximation Knowledge Engineering Technique Linear Regression Machine Learning Regression Algorithm Meteorological Factors Numerical Analysis Pattern Classification Power Engineering Computing Power Grid Power Grid Factors Power System Power System Planning Power System Planning And Layout Power System Reliability Random Forest Regression Analysis Regression Tree Reliability Restoration Planning Statistics Storms Support Vector Machine Support Vector Regression Typhoon Power Outages
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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