117378 20230519145545.0 doi 10.3390/en14164776 sideral 127071 ART-2021-127071 eng Miraftabzadeh S.M. Advances in the application of machine learning techniques for power system analytics: A survey† 2021 Access copy available to the general public Unrestricted The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.252 2021 ENERGY & FUELS 80 / 119 = 0.672 2021 Q3 T3 0.653 2021 Energy (miscellaneous) 2021 Q1 Energy Engineering and Power Technology 2021 Q1 Fuel Technology 2021 Q1 Control and Optimization 2021 Q1 Engineering (miscellaneous) 2021 Q1 5.0 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Longo M. Foiadelli F. Pasetti M. Igual R. Universidad de Zaragoza (orcid)0000-0002-1561-0536 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica 14, 16 (2021), 4776 [24 pp] ENERGIES Energies 1996-1073 1298092 http://zaguan.unizar.es/record/117378/files/texto_completo.pdf Versión publicada 2656350 http://zaguan.unizar.es/record/117378/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:117378 articulos driver 2023-05-18-15:43:40 ARTICLE