A Novel Method of Statistical Line Loss Estimation for Distribution Feeders Based on Feeder Cluster and Modified XGBoost
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Cited by:
- Gheorghe Grigoras & Bogdan-Constantin Neagu, 2019. "Smart Meter Data-Based Three-Stage Algorithm to Calculate Power and Energy Losses in Low Voltage Distribution Networks," Energies, MDPI, vol. 12(15), pages 1-27, August.
- Mirosław Kornatka & Anna Gawlak, 2021. "An Analysis of the Operation of Distribution Networks Using Kernel Density Estimators," Energies, MDPI, vol. 14(21), pages 1-12, October.
- Kun Li & Junsan Zhao & Yilin Lin, 2023. "Debris-flow susceptibility assessment in Dongchuan using stacking ensemble learning including multiple heterogeneous learners with RFE for factor optimization," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 2477-2511, September.
- Ma, Chenjie & Menke, Jan-Hendrik & Dasenbrock, Johannes & Braun, Martin & Haslbeck, Matthias & Schmid, Karl-Heinz, 2019. "Evaluation of energy losses in low voltage distribution grids with high penetration of distributed generation," Applied Energy, Elsevier, vol. 256(C).
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Keywords
loss estimation; line loss; distribution system; eXtreme Gradient Boosting (XGBoost); k -medoids; feeder cluster;All these keywords.
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