000117378 001__ 117378
000117378 005__ 20230519145545.0
000117378 0247_ $$2doi$$a10.3390/en14164776
000117378 0248_ $$2sideral$$a127071
000117378 037__ $$aART-2021-127071
000117378 041__ $$aeng
000117378 100__ $$aMiraftabzadeh S.M.
000117378 245__ $$aAdvances in the application of machine learning techniques for power system analytics: A survey†
000117378 260__ $$c2021
000117378 5060_ $$aAccess copy available to the general public$$fUnrestricted
000117378 5203_ $$aThe 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.
000117378 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000117378 590__ $$a3.252$$b2021
000117378 592__ $$a0.653$$b2021
000117378 594__ $$a5.0$$b2021
000117378 591__ $$aENERGY & FUELS$$b80 / 119 = 0.672$$c2021$$dQ3$$eT3
000117378 593__ $$aEnergy (miscellaneous)$$c2021$$dQ1
000117378 593__ $$aEnergy Engineering and Power Technology$$c2021$$dQ1
000117378 593__ $$aFuel Technology$$c2021$$dQ1
000117378 593__ $$aControl and Optimization$$c2021$$dQ1
000117378 593__ $$aEngineering (miscellaneous)$$c2021$$dQ1
000117378 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000117378 700__ $$aLongo M.
000117378 700__ $$aFoiadelli F.
000117378 700__ $$aPasetti M.
000117378 700__ $$0(orcid)0000-0002-1561-0536$$aIgual R.$$uUniversidad de Zaragoza
000117378 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000117378 773__ $$g14, 16 (2021), 4776 [24 pp]$$pENERGIES$$tEnergies$$x1996-1073
000117378 8564_ $$s1298092$$uhttps://zaguan.unizar.es/record/117378/files/texto_completo.pdf$$yVersión publicada
000117378 8564_ $$s2656350$$uhttps://zaguan.unizar.es/record/117378/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000117378 909CO $$ooai:zaguan.unizar.es:117378$$particulos$$pdriver
000117378 951__ $$a2023-05-18-15:43:40
000117378 980__ $$aARTICLE