Prediction of Power Outage Quantity of Distribution Network Users under Typhoon Disaster Based on Random Forest and Important Variables

Author:

Li Min1,Hou Hui2ORCID,Yu Jufang2ORCID,Geng Hao23,Zhu Ling1,Huang Yong45,Li Xianqiang2

Affiliation:

1. Guangdong Power Grid Co., LTD., Guangzhou 510080, China

2. School of Automation, Wuhan University of Technology, Wuhan 430070, China

3. Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming 650200, China

4. GuangDong Power GRID Co., Ltd., Electric Power Research Institute, Guangzhou 510080, China

5. Power Remote Sensing Technology Joint Laboratory of China Southern Power Grid, Guangzhou 510080, China

Abstract

Typhoons can have disastrous effects on power systems. They may lead to a large number of power outages for distribution network users. Therefore, this paper establishes a model to predict the power outage quantity of distribution network users under a typhoon disaster. Firstly, twenty-six explanatory variables (called global variables) covering meteorological factors, geographical factors, and power grid factors are considered as the input variables. On this basis, the correlation between each explanatory variable and response variable is analyzed. Secondly, we established a global variable model to predict the power outage quantity of distribution network users based on Random Forest (RF) algorithm. Then the importance of each explanatory variable is mined to extract the most important variables. To reduce the complexity of the model and ease the burden of data collection, eight variables are eventually selected as important variables. Afterward, we predict the power outage quantity of distribution network users again using the eight important variables. Thirdly, we compare the prediction accuracy of a model called the No-model that has been used before, Linear Regression (LR), Support Vector Regression (SVR), Decision Tree Regression (DTR), RF-global variable model, and RF-important variable model. Simulation results show that the RF-important variable model proposed in this paper has a better effect. Since fewer variables can save prediction time and make the model simplified, it is recommended to use the RF-important variable model.

Funder

Ministry of Education of the People's Republic of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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