Solar Radiation Time-Series Prediction Based on Empirical Mode Decomposition and Artificial Neural Networks

  • Petros-Fotios Alvanitopoulos
  • Ioannis Andreadis
  • Nikolaos Georgoulas
  • Michalis Zervakis
  • Nikolaos Nikolaidis
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 436)


This paper presents a new model for daily solar radiation prediction. In order to capture the hidden knowledge of existing data, a time-frequency analysis on past measurements of the solar energy density is carried out. The Hilbert-Huang transform (HHT) is employed for the representation of the daily solar irradiance time series. A set of physical measurements and simulated signals are selected for the time series analysis. The empirical mode decomposition is applied and the adaptive basis of each raw signal is extracted. The decomposed narrow-band amplitude and frequency modulated signals are modelled by using dynamic artificial neural networks (ANNs). Nonlinear autoregressive networks are trained with the average daily solar irradiance as exogenous (independent) input. The instantaneous value of solar radiation density is estimated based on previous values of the time series and previous values of the independent input. The results are promising and they reveal that the proposed system can be incorporated in intelligent systems for better load management in photovoltaic systems.


Solar Radiation Solar Irradiance Empirical Mode Decomposition Integrate Water Resource Management Intrinsic Mode Function 
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  1. 1.
    Batlles, F.J., Rubio, M.A., Tovar, J., Olmo, F.J., Alados-Arboledas, L.: Empirical modelling of hourly direct irradiance by means of hourly global irradiance. Energy 25, 675–688 (2000)CrossRefGoogle Scholar
  2. 2.
    Loutzenhier, P.G., Manz, H., Felsmann, C., Strachan, P.A., Frank, T., Maxwell, G.M.: Empirical validation of models to compute solar irradiance on inclined surfaces for building energy simulation. Sol Energy 81, 254–267 (2007)CrossRefGoogle Scholar
  3. 3.
    Safi, S., Zeroual, A., Hassani, M.: Prediction of global daily solar radiation using higher order statistics. Renew Energy 27, 647–666 (2002)CrossRefGoogle Scholar
  4. 4.
    Kaplanis, S., Kaplani, E.: A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values. Renew Energy 32, 1414–1425 (2007)CrossRefGoogle Scholar
  5. 5.
    Moustrisa, K., Paliatsos, A.G., Bloutsos, A., Nikolaidis, K., Koronaki, I., Kavadias, K.: Use of neural networks for the creation of hourly global and diffuse solar irradiance data at representative locations in Greece. Renew Energy 33, 928–932 (2008)CrossRefGoogle Scholar
  6. 6.
    Rehman, S., Mohandes, M.: Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36, 571–576 (2008)CrossRefGoogle Scholar
  7. 7.
    Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society, London, Series A, pp. 903–995 (1998)Google Scholar
  8. 8.
    Siegelmann, H.T., Horne, B.G., Giles, C.L.: Computational capabilities of recurrent narx neural networks. IEEE Trans. Syst., Man Cybern., pt. B 27, 208 (1997)CrossRefGoogle Scholar
  9. 9.
    Gao, Y., Er, M.J.: NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches. Fuzzy Sets and Systems 150, 331–350 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
  11. 11.
    Moirogiorgou, K., Efstathiou, D., Zervakis, M., Nikolaidis, N.P., Stamatellos, G., Andreadis, I., Georgoulas, N., Savakis, A.E.: High Frequency Monitoring System for Integrated Water Resources Management of Rivers. In: 1st EWaS-MED International Conference: Improving Efficiency of Water Systems in a Changing Natural and Financial Environment, Thessaloniki, Greece, pp. 1–6 (April 2013)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Petros-Fotios Alvanitopoulos
    • 1
  • Ioannis Andreadis
    • 1
  • Nikolaos Georgoulas
    • 1
  • Michalis Zervakis
    • 2
  • Nikolaos Nikolaidis
    • 3
  1. 1.Department of Electrical and Computer Engineering, School of EngineeringDemocritus University of ThraceXanthiGreece
  2. 2.Laboratory of Digital Signal & Image Processing, Department of Electronic and Computer EngineeringTechnical University of Crete (TUC)ChaniaGreece
  3. 3.Laboratory of Hydrochemical Engineering and Remediation of Soil (HERSLab), Dept. of Environmental EngineeringTechnical University of Crete (TUC)ChaniaGreece

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