Modeling Daily Profiles of Solar Global Radiation Using Statistical and Data Mining Techniques

  • Pedro F. Jiménez-Pérez
  • Llanos Mora-López
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8819)

Abstract

Solar radiation forecasting is important for multiple fields, including solar energy power plants connected to grid. To address the need for solar radiation hourly forecasts this paper proposes the use of statistical and data mining techniques that allow different solar radiation hourly profiles for different days to be found and established. A new method is proposed for forecasting solar radiation hourly profiles using daily clearness index. The proposed method was checked using data recorded in Malaga. The obtained results show that it is possible to forecast hourly solar global radiation for a day with an energy error around 10% which means a significant improvement on previously reported errors.

Keywords

k-means clearness index forecasting hourly solar radiation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Luque, A., Hegedus, S.: Handbook of photovoltaic science and engineering. John Wiley & Sons Ltd., Berlin (2002)Google Scholar
  2. 2.
    Chang, T.P.: Output energy of a photovoltaic module mounted on a single-axis tracking system. Applied Energy 86, 2071–2078 (2009)CrossRefGoogle Scholar
  3. 3.
    Box, G.E.P., Jenkins, G.M.: Time Series Analysis forecasting and control. Prentice Hall (1976)Google Scholar
  4. 4.
    De Gooijer, J.G., Hyndman, R.J.: 25 years of iif time series forecasting: A selective review. Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics (2005)Google Scholar
  5. 5.
    Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Springer Texts in Statistics (2002)Google Scholar
  6. 6.
    Brinkworth, B.J.: Autocorrelation and stochastic modelling of insolation sequences. Solar Energy 19, 343–347 (1997)CrossRefGoogle Scholar
  7. 7.
    Bartoli, B., Coluaai, B., Cuomo, V., Francesca, M., Serio, C.: Autocorrelation of daily global solar radiation. Il nuovo cimento 40, 113–122 (1983)Google Scholar
  8. 8.
    Aguiar, R., Collares-Pereira, M., Conde, J.P.: Simple procedure for generating sequences of daily radiation values using a library of markov transition matrices. Solar Energy 4(3), 269–279 (1988)CrossRefGoogle Scholar
  9. 9.
    Graham, V.A., Hollands, K.G.T., Unny, T.E.A.: A time series model for kt with application to global synthetic weather generation. Solar Energy 40, 83–92 (1988)CrossRefGoogle Scholar
  10. 10.
    Aguiar, R.J., Collares-Pereira, M.: TAG: A time dependent autoregressive gaussian model for generating synthetic hourly radiation. Solar Energy 49(3), 167–174 (1992)CrossRefGoogle Scholar
  11. 11.
    Mora-López, L., Sidrach de Cardona, M.: Multiplicative arma models to generate hourly series of global irradiation. Solar Energy 63, 283–291 (1998)CrossRefGoogle Scholar
  12. 12.
    Perez, R., et al.: Forecasting solar radiation preliminary evaluation of an approach based upon the national forecast database. Solar Energy 81(6), 809–812 (2007)CrossRefGoogle Scholar
  13. 13.
    Mora-López, L., Mora, J., Sidrach de Cardona, M., Morales-Bueno, R.: Modelling time series of climatic parameters with probabilistic finite automata. Environmental modelling and software 20(6), 753–760 (2005)CrossRefGoogle Scholar
  14. 14.
    Viorel, B.: Modeling Solar Radiation at the Earths Surface. Recent Advances. Springer (2008)Google Scholar
  15. 15.
    Guarnieri, R.A., Pereira, E.B., Chou, S.C.: Solar radiation forecast using articial neural networks in south brazil. In: 8 ICSHMO, INPE, Foz do Iguau, Brasil, April 24-28, pp. 1777–1785 (2008)Google Scholar
  16. 16.
    Heinemann, D., Lorenz, E., Girodo, M.: Forecasting of solar radiation, solar energy resource management for electricity generation from local level to global scale. Nova, Hauppauge (2005)Google Scholar
  17. 17.
    Mellit, A., Pavan, A.M.: A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected {PV} plant at trieste, italy. Solar Energy 84(5), 807–821 (2010)CrossRefGoogle Scholar
  18. 18.
    Mora-López, L., Martínez-Marchena, I., Piliougine, M., Sidrach-de-Cardona, M.: Binding statistical and machine learning models for short-term forecasting of global solar radiation. In: Gama, J., Bradley, E., Hollmén, J. (eds.) IDA 2011. LNCS, vol. 7014, pp. 294–305. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Koca, A., Oztop, H.F., Varol, Y., Koca, G.O.: Estimation of solar radiation using artificial neural networks with different input parameters for mediterranean region of anatolia in turkey. Expert Systems with Applications 38(7), 8756–8762 (2011)CrossRefGoogle Scholar
  20. 20.
    Reikard, G.: Predicting solar radiation at high resolutions: A comparison of time series forecast. Solar Energy 83, 342–349 (2009)CrossRefGoogle Scholar
  21. 21.
    Voyant, C., Paoli, C., Muselli, M., Nivet, M.-L.: Multi-horizon solar radiation forecasting for mediterranean locations using time series models. Renewable and Sustainable Energy Reviews 28, 44–52 (2013)CrossRefGoogle Scholar
  22. 22.
    Bendt, P., Collares-Pereira, M., Rabl, A.: The frequency distribution of daily insolation values. Solar Energy 27, 1–5 (1981)CrossRefGoogle Scholar
  23. 23.
    Iqbal, M.: An introduction to solar radiation. Academic Press Inc., New York (1983)Google Scholar
  24. 24.
    Jain, A., Murty, M., Flynn, P.: Data clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999)CrossRefGoogle Scholar
  25. 25.
    Duda, R., Hart, P., Stork, D.: Pattern classification. John Wiley & Sons (2001)Google Scholar
  26. 26.
    Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: Data mining, inference and prediction. Springer (2001)Google Scholar
  27. 27.
    Rohatgi, V.K., Saleh, A.K.M.E.: An Introduction to Probability and Statistics, 2nd edn. Wiley-Interscience (2001)Google Scholar
  28. 28.
    Bennett, N.D., Croke, B.F.W., Guariso, G., Guillaume, J.H.A., Hamilton, S.H., Jakeman, A.J., Marsili-Libelli, S., Newham, L.T.H., Norton, J.P., Perrin, C., Pierce, S.A., Robson, B., Seppelt, R., Voinov, A.A., Fath, B.D., Andreassian, V.: Characterising performance of environmental models. Environmental Modelling & Software 40, 1–20 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pedro F. Jiménez-Pérez
    • 1
  • Llanos Mora-López
    • 1
  1. 1.Dpto. Lenguajes y C.Computación. ETSI InformáticaUniversidad de MálagaMálagaSpain

Personalised recommendations