Detection of Non-Technical Losses in Irrigant Consumers through Artificial Intelligence: A Pilot Study
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- Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
- Cheong Hee Park & Taegong Kim, 2020. "Energy Theft Detection in Advanced Metering Infrastructure Based on Anomaly Pattern Detection," Energies, MDPI, vol. 13(15), pages 1-10, July.
- Muhammad Salman Saeed & Mohd Wazir Mustafa & Usman Ullah Sheikh & Touqeer Ahmed Jumani & Ilyas Khan & Samer Atawneh & Nawaf N. Hamadneh, 2020. "An Efficient Boosted C5.0 Decision-Tree-Based Classification Approach for Detecting Non-Technical Losses in Power Utilities," Energies, MDPI, vol. 13(12), pages 1-19, June.
- de Oliveira Ventura, Lucas & Melo, Joel D. & Padilha-Feltrin, Antonio & Fernández-Gutiérrez, Juan Pablo & Sánchez Zuleta, Carmen C. & Piedrahita Escobar, Carlos César, 2020. "A new way for comparing solutions to non-technical electricity losses in South America," Utilities Policy, Elsevier, vol. 67(C).
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Keywords
non-technical losses; rural electrical grids; artificial intelligence; pilot study; energy for agricultural processes; energy efficiency;All these keywords.
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