Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/30978
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dc.contributor.authorCandela Esclapez, Alfredo-
dc.contributor.authorLópez García, Miguel-
dc.contributor.authorVALERO, SERGIO-
dc.contributor.authorSenabre Blanes, Carolina-
dc.contributor.otherDepartamentos de la UMH::Ingeniería Mecánica y Energíaes_ES
dc.date.accessioned2024-02-02T16:32:05Z-
dc.date.available2024-02-02T16:32:05Z-
dc.date.created2022-10-17-
dc.identifier.citationSustainability 2022, 14(20), 13339es_ES
dc.identifier.issn2071-1050-
dc.identifier.urihttps://hdl.handle.net/11000/30978-
dc.description.abstractDue to the infeasibility of large-scale electrical energy storage, electricity is generated and consumed simultaneously. Therefore, electricity entities need consumption forecasting systems to plan operations and manage supplies. In addition, accurate predictions allow renewable energies on electrical grids to be managed, thereby reducing greenhouse gas emissions. Temperature affects electricity consumption through air conditioning and heating equipment, although it is the consumer’s behavior that determines specifically to what extent. This work proposes an automatic method of processing and selecting variables, with a two-fold objective: improving both the accuracy and the interpretability of the overall forecasting system. The procedure has been tested by the predictive system of the Spanish electricity operator (Red Eléctrica de España) with regard to peninsular demand. During the test period, the forecasting error was consistently reduced for the forecasting horizon, with an improvement of 0.16% in MAPE and 59.71 MWh in RMSE. The new way of working with temperatures is interpretable, since they separate the effect of temperature according to location and time. It has been observed that heat has a greater influence than the cold. In addition, on hot days, the temperature of the second previous day has a greater influence than the previous one, while the opposite occurs on cold days.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent22es_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.subjectaccuracyes_ES
dc.subjectinterpretabilityes_ES
dc.subjectshort-term load forecastinges_ES
dc.subjecttemperature analysises_ES
dc.subjecttemperature processinges_ES
dc.subject.classificationIngeniería eléctricaes_ES
dc.subject.otherCDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnologíaes_ES
dc.titleAutomatic Selection of Temperature Variables for Short-Term Load Forecastinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.3390/su142013339es_ES
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Artículos Ingeniería Mecánica y Energía


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