o:2377 Supporting future electrical utilities: using deep learning methods in ems and dms algorithms en Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation. This necessitates the development of near real-time power system algorithms, demanding lower computational complexity regarding the power system size. Considering the growing trend in the collection of historical measurement data and recent advances in the rapidly developing deep learning field, the main goal of this paper is to provide a review of recent deep learning-based power system monitoring and optimization algorithms. Electrical utilities can benefit from this review by re-implementing or enhancing the algorithms traditionally used in energy management systems (EMS) and distribution management systems (DMS). 1552099 10.1109/INFOTEH57020.2023.10094173 1552101 2767-9454 2023-06-20T12:50:03.686Z 44 yes 46 Ognjen Kundačina The Institute for Artificial Intelligence Research and Development of Serbia 0000-0003-0198-3363 Dejan Vukobratović Faculty of Technical Sciences, University of Novi Sad person 0000-0002-5305-8420 Dragiša Mišković The Institute for Artificial Intelligence Research and Development of Serbia person 0000-0002-0455-9552 Mile Mitrović Center for Energy Systems and Technology, Skolkovo Institute of Science and Technology person 0000-0001-6691-2772 Gorana Gojić The Institute for Artificial Intelligence Research and Development of Serbia person 0000-0002-6100-5871 application/pdf 421074 https://unilib.phaidrabg.rs/o:2377 no yes 1 70 92000004 71A08 XXII međunarodni simpozijum INFOTEH-JAHORINA 2023 2023