01708nas a2200265   4500000000100000000000100001008004100002260001200043653002100055653001900076653001100095653004700106653001800153100001800171700002500189700001600214700001700230700001900247245012900266856008100395300001200476490000600488520093400494022001401428       2020                            d  c12/202010aMachine Learning10aNeural Network10aEnergy10aLarge-Scale Unbalanced Distribution System10aPhotovoltaics1 aKarar Mahmoud1 aMohamed Abdel-Nasser1 aHeba Kashef1 aDomenec Puig1 aMatti Lehtonen00aMachine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics  uhttps://www.ijimai.org/journal/sites/default/files/2020-11/ijimai_6_4_17.pdf  a157-1630 v63 aIn 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.  a1989-1660