Analysis of the Seasonality in a Geothermal System Using Projectionist and Clustering Methods

  1. Santiago Porras 1
  2. Esteban Jove 2
  3. Bruno Baruque 1
  4. José Luis Calvo-Rolle 2
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

  2. 2 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Liburua:
Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings
  1. Hugo Sanjurjo González (coord.)
  2. Iker Pastor López (coord.)
  3. Pablo García Bringas (coord.)
  4. Héctor Quintián (coord.)
  5. Emilio Corchado (coord.)

Argitaletxea: Springer International Publishing AG

ISBN: 978-3-030-86271-8,  978-3-030-86270-1

Argitalpen urtea: 2021

Orrialdeak: 500-510

Biltzarra: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)

Mota: Biltzar ekarpena

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Laburpena

The environmental impact caused by greenhouse gasses emissions derived from fossil fuels, gives rise to the promotion of green policies. In this context, geothermal energy systems has experienced a significant increase in its use. The efficiency of this technology is closely linked with factors such as ground temperature, weather and season. This work develops the analysis of the behaviour of a geothermal system placed in a bioclimatic house during one year, by means of projectionists and clustering methods.

Datuen iturria: Dialnet