Prediction of the energy demand of a hotel using an artificial intelligence-based model. Casteleiro-Roca, J., Gómez-González, J., Calvo-Rolle, J., Jove, E., Quintián, H., Martín, J., Perez, S., Diaz, B., Calero-Garcia, F., & Méndez-Perez, J. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10870 LNAI:586–596, 2018.
Prediction of the energy demand of a hotel using an artificial intelligence-based model [link]Paper  doi  abstract   bibtex   
The growth of the hotel industry in the world, is a reality that increasingly needs a greater use of energy resources, and their optimal management. Of all the available energy resources, renewable energies can give greater economic efficiency and lower environmental impact. To manage these resources it is important the availability of energy prediction models. This allows managing the demand for power and the available energy resources, to obtain maximum efficiency and stability, with the consequent economic savings. This paper focuses in the use of Artificial Intelligence methods for energy prediction in luxury hotels. As a case of study, the energy performance data used were taken from the hotel complex The Ritz-Carlton, Abama, located in the South of the island of Tenerife, in the Canary Islands, Spain. This is a high complexity infrastructure with many services that require a lot of energy, such as restaurants, kitchens, swimming pools, vehicle fleet, etc., which make the hotel a good study model for other resorts. The model developed for the artificial intelligence system is based on a hybrid topology with artificial neural networks. In this paper, the daily power demand prediction using information of last 24 h is presented. This prediction allows the development of appropriate actions to optimize energy management. © Springer International Publishing AG, part of Springer Nature 2018.
@article{casteleiro-roca_prediction_2018,
	title = {Prediction of the energy demand of a hotel using an artificial intelligence-based model},
	volume = {10870 LNAI},
	issn = {03029743},
	url = {https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85048860353&doi=10.1007%2f978-3-319-92639-1_49&partnerID=40&md5=139b0205f4e030baf77f12df91ce628f},
	doi = {10.1007/978-3-319-92639-1_49},
	abstract = {The growth of the hotel industry in the world, is a reality that increasingly needs a greater use of energy resources, and their optimal management. Of all the available energy resources, renewable energies can give greater economic efficiency and lower environmental impact. To manage these resources it is important the availability of energy prediction models. This allows managing the demand for power and the available energy resources, to obtain maximum efficiency and stability, with the consequent economic savings. This paper focuses in the use of Artificial Intelligence methods for energy prediction in luxury hotels. As a case of study, the energy performance data used were taken from the hotel complex The Ritz-Carlton, Abama, located in the South of the island of Tenerife, in the Canary Islands, Spain. This is a high complexity infrastructure with many services that require a lot of energy, such as restaurants, kitchens, swimming pools, vehicle fleet, etc., which make the hotel a good study model for other resorts. The model developed for the artificial intelligence system is based on a hybrid topology with artificial neural networks. In this paper, the daily power demand prediction using information of last 24 h is presented. This prediction allows the development of appropriate actions to optimize energy management. © Springer International Publishing AG, part of Springer Nature 2018.},
	language = {English},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	author = {Casteleiro-Roca, J.-L. and Gómez-González, J.F. and Calvo-Rolle, J.L. and Jove, E. and Quintián, H. and Martín, J.F.A. and Perez, S.G. and Diaz, B.G. and Calero-Garcia, F. and Méndez-Perez, J.A.},
	editor = {Herrero A., Quintian H., Antonio Saez J., Corchado E., de Cos Juez F.J., Villar J.R., de la Cal E.A.},
	year = {2018},
	pages = {586--596}
}

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