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dc.contributor.authorLópez, Miguel-
dc.contributor.authorVALERO, SERGIO-
dc.contributor.authorRodriguez, Ana-
dc.contributor.authorGómez Veiras, Javier-
dc.contributor.authorSenabre, Carolina-
dc.contributor.otherDepartamentos de la UMH::Ingeniería Mecánica y Energíaes_ES
dc.date.accessioned2024-02-02T16:28:05Z-
dc.date.available2024-02-02T16:28:05Z-
dc.date.created2017-09-20-
dc.identifier.citationElectric Power Systems Research, Volume 154, January 2018, Pages 401-412es_ES
dc.identifier.issn1873-2046-
dc.identifier.issn0378-7796-
dc.identifier.urihttps://hdl.handle.net/11000/30975-
dc.description.abstractThis paper presents the implementation of a new online real-time hybrid load-forecasting model based on an autoregressive model and neural networks. This new system is currently running at the Spanish Transport System Operator (REE) and provides an hourly forecast for the current day and the next nine days timely every hour for the national system as well as 18 regions of Spain. These requirements impose a heavy computational burden that needs to be considered during the design phase. The system is developed to improve forecasting accuracy specifically on difficult days like hot, cold and special days. In order to achieve this goal, a deep analysis of the temperature series from 59 stations is made for each region and the relevant series are included individually in the model. Special days are also analyzed and a thorough classification of days is proposed for the Spanish national and regional system. The model is designed and tested with data from 2005 to 2015. The results provided for the period from December 2014 to October 2015 show how the addition of the proposed model to the TSO’s ensemble causes a 5% RMSE overall error reduction and a 15% reduction on the 59 difficult days considered in the testing period.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent12es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectLoad forecastinges_ES
dc.subjectPower demandes_ES
dc.subjectNeural Network applicationes_ES
dc.subject.classificationIngeniería eléctricaes_ES
dc.subject.otherCDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnologíaes_ES
dc.titleNew online load forecasting system for the Spanish Transport System Operatores_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.epsr.2017.09.003es_ES
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