Short-term energy demand forecast in hotels using hybrid intelligent modeling

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 system...

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Autores: Gómez González, José Francisco, Casteleiro-Roca, José Luis, Calvo-Rolle, José Luis, Jove, Esteban, Quintián, Héctor, González Díaz, Benjamín Jesús, Méndez Pérez, Juan Albino
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universidad de La Laguna (ULL)
Repositorio:RIULL. Repositorio Institucional de la Universidad de La Laguna
OAI Identifier:oai:riull.ull.es:915/39052
Acesso em linha:http://riull.ull.es/xmlui/handle/915/39052
Access Level:acceso abierto
Palavra-chave:Energy forecast
Artificial neural network
Hybrid modeling
Hotel
Tourism
Support vector regression
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network_name_str España
repository_id_str
spelling Short-term energy demand forecast in hotels using hybrid intelligent modelingGómez González, José FranciscoCasteleiro-Roca, José LuisCalvo-Rolle, José LuisJove, EstebanQuintián, HéctorGonzález Díaz, Benjamín JesúsMéndez Pérez, Juan AlbinoEnergy forecastArtificial neural networkHybrid modelingHotelTourismSupport vector regressionThe 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.Ingeniería Industrial202420242019info:eu-repo/semantics/articleapplication/pdfhttp://riull.ull.es/xmlui/handle/915/39052reponame:RIULL. Repositorio Institucional de la Universidad de La Lagunainstname:Universidad de La Laguna (ULL)InglésSensors, v. 19(11) (2019)Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ESoai:riull.ull.es:915/390522026-06-22T13:13:57Z
dc.title.none.fl_str_mv Short-term energy demand forecast in hotels using hybrid intelligent modeling
title Short-term energy demand forecast in hotels using hybrid intelligent modeling
spellingShingle Short-term energy demand forecast in hotels using hybrid intelligent modeling
Gómez González, José Francisco
Energy forecast
Artificial neural network
Hybrid modeling
Hotel
Tourism
Support vector regression
title_short Short-term energy demand forecast in hotels using hybrid intelligent modeling
title_full Short-term energy demand forecast in hotels using hybrid intelligent modeling
title_fullStr Short-term energy demand forecast in hotels using hybrid intelligent modeling
title_full_unstemmed Short-term energy demand forecast in hotels using hybrid intelligent modeling
title_sort Short-term energy demand forecast in hotels using hybrid intelligent modeling
dc.creator.none.fl_str_mv Gómez González, José Francisco
Casteleiro-Roca, José Luis
Calvo-Rolle, José Luis
Jove, Esteban
Quintián, Héctor
González Díaz, Benjamín Jesús
Méndez Pérez, Juan Albino
author Gómez González, José Francisco
author_facet Gómez González, José Francisco
Casteleiro-Roca, José Luis
Calvo-Rolle, José Luis
Jove, Esteban
Quintián, Héctor
González Díaz, Benjamín Jesús
Méndez Pérez, Juan Albino
author_role author
author2 Casteleiro-Roca, José Luis
Calvo-Rolle, José Luis
Jove, Esteban
Quintián, Héctor
González Díaz, Benjamín Jesús
Méndez Pérez, Juan Albino
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería Industrial
dc.subject.none.fl_str_mv Energy forecast
Artificial neural network
Hybrid modeling
Hotel
Tourism
Support vector regression
topic Energy forecast
Artificial neural network
Hybrid modeling
Hotel
Tourism
Support vector regression
description 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 resorts.
publishDate 2019
dc.date.none.fl_str_mv 2019
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://riull.ull.es/xmlui/handle/915/39052
url http://riull.ull.es/xmlui/handle/915/39052
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sensors, v. 19(11) (2019)
dc.rights.none.fl_str_mv Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
rights_invalid_str_mv Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:RIULL. Repositorio Institucional de la Universidad de La Laguna
instname:Universidad de La Laguna (ULL)
instname_str Universidad de La Laguna (ULL)
reponame_str RIULL. Repositorio Institucional de la Universidad de La Laguna
collection RIULL. Repositorio Institucional de la Universidad de La Laguna
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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