Solutions for detection of non-technical losses in the electricity grid: A review
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DOI: 10.1016/j.rser.2017.05.193
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Cited by:
- Savian, Fernando de Souza & Siluk, Julio Cezar Mairesse & Garlet, Taís Bisognin & do Nascimento, Felipe Moraes & Pinheiro, José Renes & Vale, Zita, 2021. "Non-technical losses: A systematic contemporary article review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
- Marco Toledo-Orozco & Carlos Arias-Marin & Carlos Álvarez-Bel & Diego Morales-Jadan & Javier Rodríguez-García & Eddy Bravo-Padilla, 2021. "Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities," Energies, MDPI, vol. 14(4), pages 1-23, February.
- Alireza Vahabzadeh & Alibakhsh Kasaeian & Hasan Monsef & Alireza Aslani, 2020. "A Fuzzy-SOM Method for Fraud Detection in Power Distribution Networks with High Penetration of Roof-Top Grid-Connected PV," Energies, MDPI, vol. 13(5), pages 1-24, March.
- Gideon Otchere-Appiah & Shingo Takahashi & Mavis Serwaa Yeboah & Yuichiro Yoshida, 2021. "The Impact of Smart Prepaid Metering on Non-Technical Losses in Ghana," Energies, MDPI, vol. 14(7), pages 1-16, March.
- Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
- Stracqualursi, Erika & Rosato, Antonello & Di Lorenzo, Gianfranco & Panella, Massimo & Araneo, Rodolfo, 2023. "Systematic review of energy theft practices and autonomous detection through artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
- Kong, Jun & Jiang, Wen & Tian, Qing & Jiang, Min & Liu, Tianshan, 2023. "Anomaly detection based on joint spatio-temporal learning for building electricity consumption," Applied Energy, Elsevier, vol. 334(C).
- Konstantinos V. Blazakis & Theodoros N. Kapetanakis & George S. Stavrakakis, 2020. "Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System," Energies, MDPI, vol. 13(12), pages 1-13, June.
- Hadjout, D. & Torres, J.F. & Troncoso, A. & Sebaa, A. & Martínez-Álvarez, F., 2022. "Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market," Energy, Elsevier, vol. 243(C).
- Mikhail A. Averbukh & Nikolay A. Zhukov & Stanislav V. Khvorostenko & Vasiliy I. Panteleev, 2019. "Reducing Electric Power Losses in the System of Power Supply Due to Compensation of Higher Harmonics of Currents: Economic and Energy Efficiency Outcomes," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 396-403.
- Rubén González Rodríguez & Jamer Jiménez Mares & Christian G. Quintero M., 2020. "Computational Intelligent Approaches for Non-Technical Losses Management of Electricity," Energies, MDPI, vol. 13(9), pages 1-25, May.
- 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.
- Ahmad, Tanveer & Chen, Huanxin & Wang, Jiangyu & Guo, Yabin, 2018. "Review of various modeling techniques for the detection of electricity theft in smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2916-2933.
- 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; Commercial losses; Electricity theft; Electricity fraud; Detection; Modeling; Estimation; Literature review;All these keywords.
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