Abstract
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 Castilla-La Mancha from 2001 to 2013.
Results showed a good generalization capacity of the model, obtaining an average MAE of 1.63 \(\mathrm{MJ/m}^2\) in 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 analyzed.
This is a preview of subscription content, log in via an institution to check access.
References
Servicio de Información Agroclimática para el Regadío (SIAR) (2015). http://eportal.magrama.gob.es/websiar/Inicio.aspx
Aeileis, A., Grothendieck, G.: zoo: S3 infrastructure for regular and irregular time series. J. Stat. Softw. 14(6), 1–27 (2005). https://cran.r-project.org/package=zoo
Antonanzas-Torres, F., Martinez-de Pison, F.J., Antonanzas, J., Perpinan, O.: Downscaling of global solar irradiation in complex areas in R. J. Renew. Sustain. Energy 6, 063105 (2014)
Antonanzas-Torres, F., Urraca, R., Fernandez-Ceniceros, J., Martinez-de Pison, F.J.: Generation of daily global solar irradiation with support vector machines for regression. Energy Convers. Manage. 96, 277–286 (2015)
Besharat, F., Dehghan, A.A., Faghih, A.R.: Empirical models for estimating global solar radiation: a review and case study. Renew. Sustain. Energy Rev. 21, 798–821 (2013)
Bojanowski, J.S., Vrieling, A., Skidmore, A.K.: A comparison of data sources for creating a long-term time series of daily gridded solar radiation for Europe. Solar Energy 99, 152–171 (2014)
Chen, J.L., Li, G.S., Wu, S.J.: Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration. Energy Convers. Manage. 75, 311–318 (2013)
Chen, T., He, T., Benesty, M.: XGBoost: eXtreme Gradient Boosting (2015). https://github.com/dmlc/xgboost, R package version 0.4-2
Dahmani, K., Notton, G., Voyant, C., Dizene, R., Nivet, M.L., Paoli, C., Tamas, W.: Multilayer perceptron approach for estimating 5-min and hourly horizontal global irradiation from exogenous meteorological data in locations without solar measurements. Renew. Energy 90, 267–282 (2016)
Gueymard, C.A., Ruiz-Arias, J.A.: Extensive worldwide validation andclimate sensitivity analysis of direct irradiance predictions from1-min global irradiance. Solar Energy 128, 1–30 (2016). http://www.sciencedirect.com/science/article/pii/S0038092X15005435, Special Issue: Progress in Solar Energy
Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2001)
Kisi, O.: Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach. Energy 64, 429–436 (2014)
Paulescu, M., Paulescu, E., Gravila, P., Badescu, V.: Solar radiation measurements. In: Paulescu, M., et al. (eds.) Weather Modeling and Forecasting of PV Systems Operation. Green Energy and Technology, pp. 17–42. Springer, London (2013)
Perpiñán, O.: Solar radiation and photovoltaic systems with R. J. Stat. Softw. 50(9), 1–32 (2012). https://cran.r-project.org/web/packages/solaR/index.html
R Core Team: R: A Language and Environment for StatisticalComputing. R Foundation for Statistical Computing, Vienna, Austria (2014). http://www.R-project.org/
Urraca, R., Antonanzas, J., Martinez-de Pison, F.J., Antonanzas-Torres, F.: Estimation of solar global irradiation in remote areas. J. Renew. Sustain. Energy 7(2), 1–14 (2015)
Wickham, H.: ggplot2: Elegant Graphics For Data Analysis. Springer, New York (2009). http://had.co.nz/ggplot2/book
Acknowledgments
J. Antonanzas and R. Urraca would like to acknowledge the fellowship FPI-UR-2014 granted by the University of La Rioja. F. Antonanzas-Torres would like to express his gratitude for the FPI-UR-2012 and ATUR grant No. 03061402 at the University of La Rioja. Finally, all the authors are greatly indebted to the Agencia de Desarrollo Economico de La Rioja for the ADER-2012-I-IDD-00126 (CONOBUILD) fellowship for funding parts of this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Urraca, R., Antonanzas, J., Antonanzas-Torres, F., Martinez-de-Pison, F.J. (2017). Estimation of Daily Global Horizontal Irradiation Using Extreme Gradient Boosting Machines. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-47364-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47363-5
Online ISBN: 978-3-319-47364-2
eBook Packages: EngineeringEngineering (R0)