@article{2803,
  keywords = {Machine Learning, Neural Network, Energy, Large-Scale Unbalanced Distribution System, Photovoltaics},
  author = {Karar Mahmoud and Mohamed Abdel-Nasser and Heba Kashef and Domenec Puig and Matti Lehtonen},
  title = {Machine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics},
  abstract = {In the recent years, the penetration of photovoltaics (PV) has obviously been increased in unbalanced power distribution systems. Driven by this trend, comprehensive simulation tools are required to accurately analyze large-scale distribution systems with a fast-computational speed. In this paper, we propose an efficient method for performing time-series simulations for unbalanced power distribution systems with PV. Unlike the existing iterative methods, the proposed method is based on machine learning. Specifically, we propose a fast, reliable and accurate method for determining energy losses in distribution systems with PV. The proposed method is applied to a large-scale unbalanced distribution system (the IEEE 906 Bus European LV Test Feeder) with PV grid-connected units. The method is validated using OpenDSS software. The results demonstrate the high accuracy and computational performance of the proposed method.},
  year = {2020},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {6},
  number = {4},
  pages = {157-163},
  month = {12/2020},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2020-11/ijimai_6_4_17.pdf},
  doi = {10.9781/ijimai.2020.08.002},
}