An Efficient Boosted C5.0 Decision-Tree-Based Classification Approach for Detecting Non-Technical Losses in Power Utilities
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- Depuru, Soma Shekara Sreenadh Reddy & Wang, Lingfeng & Devabhaktuni, Vijay, 2011. "Smart meters for power grid: Challenges, issues, advantages and status," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2736-2742, August.
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- Kessides, Ioannis N., 2013. "Chaos in power: Pakistan's electricity crisis," Energy Policy, Elsevier, vol. 55(C), pages 271-285.
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- Mehmet Efe Biresselioglu & Muhittin Hakan Demir, 2022. "Constructing a Decision Tree for Energy Policy Domain Based on Real-Life Data," Energies, MDPI, vol. 15(7), pages 1-15, March.
- Vanessa Gindri Vieira & Daniel Pinheiro Bernardon & Vinícius André Uberti & Rodrigo Marques de Figueiredo & Lucas Melo de Chiara & Juliano Andrade Silva, 2023. "Detection of Non-Technical Losses in Irrigant Consumers through Artificial Intelligence: A Pilot Study," Energies, MDPI, vol. 16(19), pages 1-17, September.
- Alaa M. Elsayad & Ahmed M. Nassef & Mujahed Al-Dhaifallah & Khaled A. Elsayad, 2020. "Classification of Biodegradable Substances Using Balanced Random Trees and Boosted C5.0 Decision Trees," IJERPH, MDPI, vol. 17(24), pages 1-20, December.
- Muhammad Salman Saeed & Mohd Wazir Mustafa & Nawaf N. Hamadneh & Nawa A. Alshammari & Usman Ullah Sheikh & Touqeer Ahmed Jumani & Saifulnizam Bin Abd Khalid & Ilyas Khan, 2020. "Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review," Energies, MDPI, vol. 13(18), pages 1-25, September.
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Keywords
machine learning algorithms; artificial intelligence; electricity theft detection; boosted C5.0 decision tree; power utilities;All these keywords.
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