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All issues Volume 626 (2025) E3S Web Conf., 626 (2025) 04003 References
Open Access
Issue
E3S Web Conf.
Volume 626, 2025
International Conference on Energy, Infrastructure and Environmental Research (EIER 2025)
Article Number 04003
Number of page(s) 6
Section Computational Technologies in Electrical and Energy Systems
DOI https://doi.org/10.1051/e3sconf/202562604003
Published online 15 April 2025
  1. D. A. Copp, T. A. Nguyen, R. H. Byrne, and B. R. Chalamala, “Optimal sizing of distributed energy resources for planning 100% renewable electric power systems,” Energy, vol. 239, p. 122436, 2022. [CrossRef] [Google Scholar]
  2. G. Zheng, G. Chen, R. Deng, J. Yi, L. Huang, and J. Zhang, “The prediction method of distribution network loss under distributed power supply access,” in 2023 3rd Int. Conf. New Energy and Power Engineering (ICNEPE), pp. 472–476, Nov. 2023. [Google Scholar]
  3. J. H. Menke, N. Bornhorst, and M. Braun, “Distribution system monitoring for smart power grids with distributed generation using artificial neural networks,” Int. J. Electr. Power Energy Syst., vol. 113, pp. 472–480, Dec. 2019. [CrossRef] [Google Scholar]
  4. A. Sen, C. Andic, E. Aydin, M. Purlu, and B. Turkay, “Forecasting of wind turbine output power with artificial neural network in Izmir, Tu¨rkiye,” in 2023 14th Int. Conf. Electrical and Electronics Engineering (ELECO), pp. 1–5, Nov. 2023. [Google Scholar]
  5. X. Ma, C. Liang, X. Dong, Y. Li, and R. Xu, “A line loss prediction method based on neural network,” in 2022 3rd Int. Conf. Advanced Electrical and Energy Systems (AEES), pp. 249–254, Sep. 2022. [Google Scholar]
  6. J. Zhang, L. Wang, Y. Geng, M. Ren, J. Ma, and Y. Niu, “Line loss prediction of low voltage distributions considering mass PV and electric heating,” in 2023 6th Int. Conf. Energy, Electrical and Power Engineering (CEEPE), pp. 1041–1046, May 2023. [Google Scholar]
  7. P. K. Shukla and K. Deepa, “Deep learning techniques for transmission line fault classification – A comparative study. Ain Shams Engineering Journal, Vol. 15, No. 2, February 2024, (p. 102427). [CrossRef] [Google Scholar]
  8. L. Huang, G. Zhou, J. Zhang, Y. Zeng, and L. Li, “Calculation method of theoretical line loss in lowvoltage grids based on improved random forest algorithm,” Energies, vol. 16, no. 7, p. 2971, Jul. 2023. [CrossRef] [Google Scholar]
  9. P. Suanpang and P. Jamjuntr, “Machine learning models for solar power generation forecasting in microgrid application: Implications for smart cities,” Sustainability, vol. 16, no. 16, p. 6087, Aug. 2024. [CrossRef] [Google Scholar]
  10. A. Hussain, S. Shah, and S. Arif, “Heuristic optimisation-based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks,” IET Smart Grid, v.2, no.2, pp.269–282, 2019. [CrossRef] [Google Scholar]
  11. W. Haider and Q. Ha, “Maximum Power Penetration of Distributed Energy Resources with Sizing and Location,” in The 10th IEEE Int. Conf. Sustainable Technology and Engineering (i-COSTE 2024), 18-20 Dec. 2024, Perth, Australia. To appear. [Google Scholar]
  12. M. Purlu and B. E. Turkay, “Optimal allocation of renewable distributed generations using heuristic methods to minimize annual energy losses and voltage deviation index,” IEEE Access, vol. 10, pp. 21455–21474, 2022. [CrossRef] [Google Scholar]
  13. E. D. Melaku, E. S. Bayu, C. Roy, A. Ali, and B. Khan, “Distribution network forecasting and expansion planning with optimal location and sizing of solar photovoltaic-based distributed generation,” Computers and Electr. Eng., vol. 110, p. 108862, Jul. 2023. [CrossRef] [Google Scholar]
  14. J. Fu, Y. Han, W. Li, Y. Feng, A. S. Zalhaf, S. Zhou, P. Yang, and C. Wang, “A novel optimization strategy for line loss reduction in distribution networks with large penetration of distributed generation,” Int. J. Electr. Power Energy Syst., vol. 150, p. 109112, Aug. 2023. [CrossRef] [Google Scholar]
  15. J. Li, S. Li, W. Zhao, J. Li, K. Zhang, and Z. Jiang, “Distribution network line loss analysis method based on improved clustering algorithm and isolated forest algorithm,” Sci. Rep., vol. 14, no. 1, p. 19554, 2024. [CrossRef] [Google Scholar]
  16. A. Natekin and A. Knoll, “Gradient boosting machines, a tutorial,” Front. Neurorobotics, vol. 7, p. 21, 2013. [CrossRef] [Google Scholar]
  17. S. Suthaharan and S. Suthaharan, “Decision tree learning,” in Machine Learning Models and Algorithms for Big Data Classification, S. Suthaharan, Ed., pp. 237–269, 2016. [Google Scholar]
  18. H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola and V. Vapnik, “Support vector regression machines”, in Proc. the 9th Int. Conf. Neural Information Processing Systems, 1996, pp. 155–161. [Google Scholar]

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