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dc.contributor.authorLópez García, Miguel-
dc.contributor.authorVALERO, SERGIO-
dc.contributor.authorSans Treserras, Carlos-
dc.contributor.authorSenabre, Carolina-
dc.date.accessioned2024-02-07T13:36:10Z-
dc.date.available2024-02-07T13:36:10Z-
dc.date.created2020-12-26-
dc.identifier.citationEnergies 2021, 14, 95es_ES
dc.identifier.issn1966-1073-
dc.identifier.urihttps://hdl.handle.net/11000/31226-
dc.description.abstractThis paper introduces a new methodology to include daylight information in short-term load forecasting (STLF) models. The relation between daylight and power consumption is obvious due to the use of electricity in lighting in general. Nevertheless, very few STLF systems include this variable as an input. In addition, an analysis of one of the current STLF models at the Spanish Transmission System Operator (TSO), shows two humps in its error profile, occurring at sunrise and sunset times. The new methodology includes properly treated daylight information in STLF models in order to reduce the forecasting error during sunrise and sunset, especially when daylight savings time (DST) one-hour time shifts occur. This paper describes the raw information and the linearization method needed. The forecasting model used as the benchmark is currently used at the TSO’s headquarters and it uses both autoregressive (AR) and neural network (NN) components. The method has been designed with data from the Spanish electric system from 2011 to 2017 and tested over 2018 data. The results include a justification to use the proposed linearization over other techniques as well as a thorough analysis of the forecast results yielding an error reduction in sunset hours from 1.56% to 1.38% for the AR model and from 1.37% to 1.30% for the combined forecast. In addition, during the weeks in which DST shifts are implemented, sunset error drops from 2.53% to 2.09%.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent14es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_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.subjectdaylightes_ES
dc.subjectload forecastinges_ES
dc.subjectpower demandes_ES
dc.subject.classificationIngeniería Mecánicaes_ES
dc.subject.otherCDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnología::621 - Ingeniería mecánica en general. Tecnología nuclear. Electrotecnia. Maquinariaes_ES
dc.titleUse of Available Daylight to Improve Short-Term Load Forecasting Accuracyes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://dx.doi.org/10.3390/en14010095es_ES
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Artículos Ingeniería Mecánica y Energía


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