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A Solar Thermal System Temperature Prediction of a Smart Building for Data Recovery and Security Purposes

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12490))

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Abstract

This paper perform a comparison between different clustering algorithms, that their optimal number of clusters has been calculated throw different performance measurements. The comparison takes into account the prediction of the thermal solar panel output temperature to conclude what is the best clustering division. The used dataset is extracted from a Bioclimatic house that belongs to Sotavento Galicia Foundation, and it is composed of the most important variables in the thermal solar energy generation system.

Silhouette, Calinski-Harabasz, and Davies-Bouldin were used to achieve the optimal number of clusters and then, Artificial Neural Networks and Polynomial Regression were trained, with several configurations, to create a hybrid intelligent model for regression. Very good results were obtained with this procedure, that allows to reduce the computational cost of creating a hybrid model without knowing the number of clusters for the dataset.

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Acknowledgments

Junta de Castilla y León - Consejería de Educación. Project: LE078G18- UXXI2018/000149. U-220.

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Authors and Affiliations

  1. University of A Coruña, CTC, Department of Industrial Engineering, CITIC, Avda. 19 de Febrero S/n, 15405, Ferrol, A Coruña, Spain

    José-Luis Casteleiro-Roca, Esteban Jove, Francisco Zayas-Gato, Héctor Quintián & José Luis Calvo-Rolle

  2. University of León, Department of Electrical and Systems Engineering, Escuela de Ingenierías, Campus de Vegazana, 24071, León, Spain

    María Teresa García-Ordás & Héctor Alaiz-Moretón

Authors
  1. José-Luis Casteleiro-Roca
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  2. María Teresa García-Ordás
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  3. Esteban Jove
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  4. Francisco Zayas-Gato
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  5. Héctor Quintián
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  6. Héctor Alaiz-Moretón
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  7. José Luis Calvo-Rolle
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Corresponding author

Correspondence to José-Luis Casteleiro-Roca .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Casteleiro-Roca, JL. et al. (2020). A Solar Thermal System Temperature Prediction of a Smart Building for Data Recovery and Security Purposes. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_44

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  • DOI: https://doi.org/10.1007/978-3-030-62365-4_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62364-7

  • Online ISBN: 978-3-030-62365-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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