Estimation of Daily Global Horizontal Irradiation Using Extreme Gradient Boosting Machines

  1. Ruben Urraca 1
  2. Javier Antonanzas 1
  3. Fernando Antonanzas-Torres 1
  4. Francisco Javier Martinez-de-Pison 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Libro:
International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings
  1. Manuel Graña (coord.)
  2. José Manuel López-Guede (coord.)
  3. Oier Etxaniz (coord.)
  4. Álvaro Herrero (coord.)
  5. Héctor Quintián (coord.)
  6. Emilio Corchado (coord.)

Editorial: Springer Suiza

ISBN: 978-3-319-47364-2 3-319-47364-6 978-3-319-47363-5 3-319-47363-8

Año de publicación: 2017

Páginas: 105-113

Congreso: International Conference on Computational Intelligence in Security for Information Systems (9. 2016. San Sebastián)

Tipo: Aportación congreso

beta Ver similares en nube de resultados
DOI: 10.1007/978-3-319-47364-2_11 WoS: WOS:000405330000011 DIALNET GOOGLE SCHOLAR

Resumen

Empirical models are widely used to estimate solar radiation at locations where other more readily available meteorological variables are recorded. Within this group, soft computing techniques are the ones that provide more accurate results as they are able to relate all recorded variables with solar radiation. In this work, a new implementation of Gradient Boosting Machines (GBMs) named XGBoost is used to predict daily global horizontal irradiation at locations where no pyranometer records are available. The study is conducted with data from 38 ground stations in Castile-La Mancha from 2001 to 2013.Results showed a good generalization capacity of the model, obtain-inganaverageMAEof1.63MJ/m2in stations not used to calibrate the model, and thus outperforming other statistical models found in the literature for Spain. A detailed error analysis was performed to understand the distribution of errors according to the clearness index and level of radiation. Moreover, the contribution of each input was also analysed.