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dc.contributor.authorCasteleiro-Roca, José-Luis
dc.contributor.authorGómez-González, José-Francisco
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.date.accessioned2019-07-08T08:39:55Z
dc.date.available2019-07-08T08:39:55Z
dc.date.issued2019
dc.identifier.citationCasteleiro-Roca, J.; Gómez-González, J.F.; Calvo-Rolle, J.L.; Jove, E.; Quintián, H.; Gonzalez Diaz, B.; Mendez Perez, J.A. Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling. Sensors 2019, 19, 2485es_ES
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/2183/23424
dc.descriptionThis paper is the extension of the conference paper: Casteleiro-Roca, J.-L.; Gómez-González, J.F.; Calvo-Rolle, J.L.; Jove, E.; Quintián, H.; Acosta Martín, J.F.; Gonzalez Perez, S.; Gonzalez Diaz, B.; Calero-Garcia, F. and Méndez-Perez, J.A. Prediction of the Energy Demand of a Hotel Using an Artificial Intelligence-Based Model. In Proceedings of the 13th International Conference, Hybrid Artificial Intelligent Systems (HAIS), Oviedo, Spain, 20–22 June 2018.es_ES
dc.description.abstract[Abstract] The 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 resortses_ES
dc.description.sponsorshipFundación CajaCanarias; grant number PR705752es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.urihttps://doi.org/10.3390/s19112485es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectEnergy forecastes_ES
dc.subjectArtificial neural networkses_ES
dc.subjectHybrid modelinges_ES
dc.subjectSupport vector regressiones_ES
dc.subjectHoteles_ES
dc.subjectTourismes_ES
dc.titleShort-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modelinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleSensorses_ES
UDC.volume19es_ES
UDC.issue11es_ES
UDC.startPage2485es_ES


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