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Intelligent Model Hotel Energy Demand Forecasting by Means of LSTM and GRU Neural Networks

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Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference (DCAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 585))

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Abstract

The hotel business consumes a significant amount of energy, requiring effective management solutions to ensure its performance and sustainability. The increased position of hotels as prosumers plus the renewable energy technologies, complicate the design of these systems, which depends on the use of reliable predict6ions for energy load. Based on artificial neural networks (ANN), regression approaches such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit networks (GRU), this research proposes an intelligent model for predicting energy demand in a hotel. Validation was performed using real hotel data likened to time-series models. The resulting forecasts were remarkable, indicating a promising potential for its usage in hotel energy management systems.

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Acknowledgements

CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01).

Author information

Authors and Affiliations

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

    Víctor López, José-Luis Casteleiro-Roca, Francisco Zayas Gato & Jose Luis Calvo-Rolle

  2. Department of Computer Science and System Engineering, University of La Laguna, Tenerife, Spain

    Juan Albino Mendez Perez

Authors
  1. Víctor López
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  2. José-Luis Casteleiro-Roca
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  3. Francisco Zayas Gato
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  4. Juan Albino Mendez Perez
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  5. Jose Luis Calvo-Rolle
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Corresponding author

Correspondence to Víctor López .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    José Manuel Machado

  2. University of Salamanca, Salamanca, Spain

    Pablo Chamoso

  3. University of Salamanca, Salamanca, Spain

    Guillermo Hernández

  4. Koszalin University of Technology, Koszalin, Poland

    Grzegorz Bocewicz

  5. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Roussanka Loukanova

  6. University of Coruña, La Coruña, Spain

    Esteban Jove

  7. Departamento de Matemática Aplicada, University of Salamanca, Salamanca, Spain

    Angel Martin del Rey

  8. University of Calabria, Arcavacata di Rende, Cosenza, Italy

    Michela Ricca

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López, V., Casteleiro-Roca, JL., Gato, F.Z., Perez, J.A.M., Calvo-Rolle, J.L. (2023). Intelligent Model Hotel Energy Demand Forecasting by Means of LSTM and GRU Neural Networks. In: Machado, J.M., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-031-23210-7_8

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