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International Conference on Hybrid Artificial Intelligence Systems

HAIS 2018: Hybrid Artificial Intelligent Systems pp 586–596Cite as

Prediction of the Energy Demand of a Hotel Using an Artificial Intelligence-Based Model

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10870))

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.

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Acknowledgments

This study was supported by CajaCanarias Foundation with the project PR705752 (GreenTourist, 2016TUR17) and The Ritz-Carlton Abama Hotel in Tenerife, Spain.

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Authors and Affiliations

  1. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José-Luis Casteleiro-Roca, José Luis Calvo-Rolle, Esteban Jove, Héctor Quintián & Francisco Calero-Garcia

  2. Department of Computer Science and System Engineering, Universidad de La Laguna, La Laguna, Spain

    José-Luis Casteleiro-Roca, Esteban Jove, Francisco Calero-Garcia & Juan Albino Méndez-Perez

  3. Department of Industrial Engineering, Universidad de La Laguna Engineering, La Laguna, Spain

    José Francisco Gómez-González, Juan Francisco Acosta Martín, Sara Gonzalez Perez, Benjamin Gonzalez Diaz & Francisco Calero-Garcia

  4. Departamento de Economia, Contabilidad y Finanzas, Universidad de La Laguna, La Laguna, Spain

    Francisco Calero-Garcia

Authors
  1. José-Luis Casteleiro-Roca
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  2. José Francisco Gómez-González
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  3. José Luis Calvo-Rolle
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  4. Esteban Jove
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  5. Héctor Quintián
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  6. Juan Francisco Acosta Martín
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  7. Sara Gonzalez Perez
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  8. Benjamin Gonzalez Diaz
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  9. Francisco Calero-Garcia
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  10. Juan Albino Méndez-Perez
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Corresponding author

Correspondence to Esteban Jove .

Editor information

Editors and Affiliations

  1. Department of Mine Operating and Prospection, University of Oviedo, Oviedo, Spain

    Francisco Javier de Cos Juez

  2. Department of Computer Science, University of Oviedo, Oviedo, Spain

    José Ramón Villar

  3. Department of Computer Science, University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of A Coruña, A Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    José António Sáez

  7. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Casteleiro-Roca, JL. et al. (2018). Prediction of the Energy Demand of a Hotel Using an Artificial Intelligence-Based Model. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_49

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  • DOI: https://doi.org/10.1007/978-3-319-92639-1_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92638-4

  • Online ISBN: 978-3-319-92639-1

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