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dc.contributor.authorGómez González, José Francisco 
dc.contributor.authorCasteleiro-Roca, José Luis
dc.contributor.authorCalvo-Rolle, José Luis
dc.contributor.authorJove, Esteban
dc.contributor.authorQuintián, Héctor
dc.contributor.authorGonzález Díaz, Benjamín
dc.contributor.authorMéndez Pérez, Juan Albino 
dc.contributor.otherIngeniería Industrial
dc.date.accessioned2024-10-08T20:09:38Z
dc.date.available2024-10-08T20:09:38Z
dc.date.issued2019
dc.identifier.issn1424-8220
dc.identifier.urihttp://riull.ull.es/xmlui/handle/915/39052
dc.description.abstractThe hotel industry is an important energy consumer that needs efficient energy management methods to guarantee its performance and sustainability. The new role of hotels as prosumers increases the difficulty in the design of these methods. Also, the scenery is more complex as renewable energy systems are present in the hotel energy mix. The performance of energy management systems greatly depends on the use of reliable predictions for energy load. This paper presents a new methodology to predict energy load in a hotel based on intelligent techniques. The model proposed is based on a hybrid intelligent topology implemented with a combination of clustering techniques and intelligent regression methods (Artificial Neural Network and Support Vector Regression). The model includes its own energy demand information, occupancy rate, and temperature as inputs. The validation was done using real hotel data and compared with time-series models. Forecasts obtained were satisfactory, showing a promising potential for its use in energy management systems in hotel resorts.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesSensors, v. 19(11) (2019)
dc.rightsLicencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
dc.titleShort-term energy demand forecast in hotels using hybrid intelligent modeling
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.3390/S19112485
dc.subject.keywordEnergy forecast
dc.subject.keywordArtificial neural network
dc.subject.keywordHybrid modeling
dc.subject.keywordHotel
dc.subject.keywordTourism
dc.subject.keywordSupport vector regression


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