Skip to main content
SpringerLink
Log in

Modeling Hourly Average Solar Radiation Time Series

  • Chapter
  • First Online:
Nonlinear Modeling of Solar Radiation and Wind Speed Time Series

Part of the book series: SpringerBriefs in Energy ((BRIEFSENERGY))

  • 818 Accesses

Abstract

This chapter deals with the problem of short-term prediction of hourly average solar radiation time series, recorded at ground level, by using embedding phase-space (EPS) models. Two different neural approaches have been considered to identify the nonlinear map underlying the identification problem, namely the neuro-fuzzy (NF) approach and the feedforward neural network (NN) approach. Performances are evaluated in terms of mae, rmse and skill index, in comparison with two popular reference models, namely the clear sky model and the \(P_{24}\) persistent model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. A.M.A. Baig, P. Achter, Prediction of hourly solar radiation using a novel hybrid model of arma and tdnn. Solar Energy 1, 119–123 (1991)

    Google Scholar 

  2. J. Wu, C.K. Chan, Prediction of hourly solar radiation using a novel hybrid model of arma and tdnn. Solar Energy 85, 808–817 (2011)

    Article  Google Scholar 

  3. Z. Nian, P. Behera, Solar radiation prediction based on recurrent neural networks trained by levenberg-marquardt backpropagation learning algorithm, in Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, pp. 1–7 (2012). doi:10.1109/ISGT.2012.6175757

  4. A. Yona, T. Senjyu, T. Funabashi, C. Kim, Determination method of insolation prediction with fuzzy and applying neural network for long-term ahead pv power output correction. IEEE Trans. Sustain. Energy 4, 527–533 (2013)

    Article  Google Scholar 

  5. J. Piri, O. Kisi, Modelling solar radiation reached to the earth using anfis, nn-arx, and empirical models (case studies: Zahedan and bojnurd stations). J. Atmos. Sol. Terr. Phys. 123, 39–47 (2015)

    Article  Google Scholar 

  6. Z. Nian, P. Behera, C. Williams, Solar radiation prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks, in IEEE International Systems Conference (SysCon) (2013)

    Google Scholar 

  7. H. Sun, D. Yan, N. Zhao, J. Zhou, Empirical investigation on modeling solar radiation series with armagarch models. Energy Convers. Manag. 92, 385–395 (2015)

    Article  Google Scholar 

  8. P. Lauret, J. Boland, B. Ridley, Bayesian statistical analysis applied to solar radiation modelling. Renew. Energy 49, 124–127 (2013)

    Article  Google Scholar 

  9. O. Kisi, Modeling solar radiation of mediterranean region in turkey by using fuzzy genetic approach. Energy Convers. Manag. 64, 429–436 (2014)

    Google Scholar 

  10. S. Navin, S. Pranshu, I. David, S. Prashant, Predicting solar generation from weather forecasts using machine learning, in IEEE International Conference on Smart Grid Communications (Smart Grid Comm), Brussels, Belgium

    Google Scholar 

  11. V. Prema, K.U. Rao, Development of statistical time series models for solar power prediction. Renew. Energy 83, 100–109 (2015)

    Article  Google Scholar 

  12. W. Yao, Z. Li, T. Xiu, Y. Lu, X. Li, New decomposition models to estimate hourly global solar radiation from the daily value. Solar Energy 120, 87–99 (2015)

    Article  Google Scholar 

  13. P. Alvanitopoulos, I. Andreadis, N. Georgoulas, M. Zervakis, N. Nikolaidis, Solar radiation prediction model based on empirical mode decomposition, in 2014 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 161–166 (2014)

    Google Scholar 

  14. L. Mazorra Aguiar, B. Pereira, M. David, F. Diaz, P. Lauret, Use of satellite data to improve solar radiation forecasting with Bayesian Artificial Neural Networks. Solar Energy 122, 1309–1324 (2015)

    Google Scholar 

  15. P. Ineichen, R. Perez, A New airmass independent formulation for the Linke turbidity coefficient. Phys. A 73, 151–157 (2002)

    Google Scholar 

  16. R. Perez, A new operational model for satellite-derived irradiances-description and validation. Solar Energy 73, 207–317 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, Catania, Italy

    Luigi Fortuna, Giuseppe Nunnari & Silvia Nunnari

Authors
  1. Luigi Fortuna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giuseppe Nunnari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Silvia Nunnari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luigi Fortuna .

Rights and permissions

Reprints and permissions

Copyright information

© 2016 The Author(s)

About this chapter

Cite this chapter

Fortuna, L., Nunnari, G., Nunnari, S. (2016). Modeling Hourly Average Solar Radiation Time Series. In: Nonlinear Modeling of Solar Radiation and Wind Speed Time Series. SpringerBriefs in Energy. Springer, Cham. https://doi.org/10.1007/978-3-319-38764-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-38764-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-38763-5

  • Online ISBN: 978-3-319-38764-2

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation