Prediction of Power Outage Quantity of Distribution Network Users under Typhoon Disaster Based on Random Forest and Important Variables
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Abstract
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DOI: 10.1155/2021/6682242
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Cited by:
- 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).
- Majah-Leah V. Ravago & Karl Robert L. Jandoc & Miah Maye Pormon, 2023. "Reliability and Forced Outages: Survival Analysis with Recurrent Events," Department of Economics, Ateneo de Manila University, Working Paper Series 202303, Department of Economics, Ateneo de Manila University.
- 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.
- 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.
- 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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