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

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  • Min Li
  • Hui Hou
  • Jufang Yu
  • Hao Geng
  • Ling Zhu
  • Yong Huang
  • Xianqiang Li

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.

Suggested Citation

  • Min Li & Hui Hou & Jufang Yu & Hao Geng & Ling Zhu & Yong Huang & Xianqiang Li, 2021. "Prediction of Power Outage Quantity of Distribution Network Users under Typhoon Disaster Based on Random Forest and Important Variables," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, January.
  • Handle: RePEc:hin:jnlmpe:6682242
    DOI: 10.1155/2021/6682242
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    Cited by:

    1. Ravago, Majah-Leah V. & Jandoc, Karl Robert & Pormon, Miah Maye, 2023. "Reliability and forced outages: Survival analysis with recurrent events," Japan and the World Economy, Elsevier, vol. 68(C).
    2. Maela Madel L. Cahigas & Ardvin Kester S. Ong & Yogi Tri Prasetyo, 2023. "Super Typhoon Rai’s Impacts on Siargao Tourism: Deciphering Tourists’ Revisit Intentions through Machine-Learning Algorithms," Sustainability, MDPI, vol. 15(11), pages 1-29, May.
    3. Tamjid Shabestari, Sara & Kasaeian, Alibakhsh & Vaziri Rad, Mohammad Amin & Forootan Fard, Habib & Yan, Wei-Mon & Pourfayaz, Fathollah, 2022. "Techno-financial evaluation of a hybrid renewable solution for supplying the predicted power outages by machine learning methods in rural areas," Renewable Energy, Elsevier, vol. 194(C), pages 1303-1325.
    4. Ravago, Majah-Leah V. & Jandoc, Karl Robert & Pormon, Miah Maye, 2023. "Reliability and forced outages: Survival analysis with recurrent events," Japan and the World Economy, Elsevier, vol. 68(C).
    5. Hyun Jin Han & Hae Sun Suh, 2023. "Predicting Unmet Healthcare Needs in Post-Disaster: A Machine Learning Approach," IJERPH, MDPI, vol. 20(19), pages 1-13, September.

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