Skip to main content

Intra-hour Forecasting of Direct Normal Solar Irradiance Using Variable Selection with Artificial Neural Networks

  • 1731 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 35)

Abstract

Renewable Energy Sources (RES) are one of the key solutions to handle the world’s future energy needs, while decreasing carbon emissions. To produce electricity, large concentrating solar power plants depend on Direct Normal Irradiance (DNI), which is rapidly variable under broken clouds conditions. To work at optimum capacity while maintaining stable grid conditions, such plants require accurate DNI forecasts for various time horizons. The main goal of this study is the forecasting of DNI over two short-term horizons: 15-min and 1-h. The proposed system is purely based on historical local data and Artificial Neural Networks (ANN). For this aim, 1-min solar irradiance measurements have been obtained from two sites in different climates. According to the forecast results, the coefficient of determination (R2) ranges between 0.500 and 0.851, the Mean Absolute Percentage Error (MAPE) between 0.500 and 0.851, the Normalized Mean Squared Error (NMSE) between 0.500 and 0.851, and the Root Mean Square Error (RMSE) between 0.065 kWh/m2 and 0.105 kWh/m2. The proposed forecasting models show a reasonably good forecasting capability, which is decisive for a good management of solar energy systems.

Keywords

  • Direct Normal Irradiance
  • Forecasting
  • Time series analysis
  • Variable selection
  • Artificial neural networks

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-73192-6_29
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   199.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-73192-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   259.00
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.

References

  • Kaur, A., Nonnenmacher, L., Pedro, H.T., Coimbra, C.F.: Benefits of solar forecasting for energy imbalance markets. Renew. Energy 86, 819–830 (2016)

    CrossRef  Google Scholar 

  • Law, E.W., Prasad, A.A., Kay, M., Taylor, R.A.: Direct normal irradiance forecasting and its application to concentrated solar thermal output forecasting–a review. Sol. Energy 108, 287–307 (2014)

    CrossRef  Google Scholar 

  • Chu, Y., Pedro, H.T., Coimbra, C.F.: Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning. Sol. Energy 98, 592–603 (2013)

    CrossRef  Google Scholar 

  • Peruchena, C.M.F., Gastón, M., Schroedter-Homscheidt, M., Kosmale, M., Marco, I.M., García-Moya, J.A., Casado-Rubio, J.L.: Dynamic paths: towards high frequency direct normal irradiance forecasts. Energy 132, 315–323 (2017)

    CrossRef  Google Scholar 

  • Gueymard, C.A.: Temporal variability in direct and global irradiance at various time scales as affected by aerosols. Sol. Energy 86(12), 3544–3553 (2012)

    CrossRef  Google Scholar 

  • Twidell, J., Weir, T.: Renewable energy resources. Routledge, London (2015)

    Google Scholar 

  • Kleissl, J.: Solar energy forecasting and resource assessment. Academic Press, Boston (2013)

    Google Scholar 

  • Jović, A., Brkić, K., Bogunović, N.: A review of feature selection methods with applications. In: Information and Communication Technology, 38th International Convention on Electronics and Microelectronics (MIPRO), pp. 1200–1205. IEEE (2015)

    Google Scholar 

  • Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)

    CrossRef  Google Scholar 

  • Ineichen, P., Perez, R.: A new airmass independent formulation for the Linke turbidity coefficient. Sol. Energy 73(3), 151–157 (2002)

    CrossRef  Google Scholar 

  • Gueymard, C.A., Ruiz-Arias, J.A.: Extensive worldwide validation and climate sensitivity analysis of direct irradiance predictions from 1-min global irradiance. Sol. Energy 128, 1–30 (2016)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hassen Bouzgou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Atmani, H., Bouzgou, H., Gueymard, C.A. (2018). Intra-hour Forecasting of Direct Normal Solar Irradiance Using Variable Selection with Artificial Neural Networks. In: Hatti, M. (eds) Artificial Intelligence in Renewable Energetic Systems. ICAIRES 2017. Lecture Notes in Networks and Systems, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-319-73192-6_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-73192-6_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73191-9

  • Online ISBN: 978-3-319-73192-6

  • eBook Packages: EngineeringEngineering (R0)