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Ensemble of Support Vector Methods to Estimate Global Solar Radiation in Algeria

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

Abstract

In this paper, we propose a set of times series forecasting techniques based on the combination of Support Vector Regression methods to predict global horizontal solar radiation in Algeria. The models were constructed and tested using different architectures of Support Vector Machine (SVM), namely, (RBF kernel, Polinomial kernel and Linear kernel). We use individual time series models and linear combination techniques to predict global solar radiation indifferent sites in Algeria. For this aim, the recorded data of 4 stations spread over Algeria were used to build different combination schemes for the different times series algorithms. The efficiency of the different models was calculated using a number of statistical indicators: the Mean Absolute Percentage Error (MAPE), the Mean Squared Error (RMSE), Mean Bias Error (MABE) and the Coefficient of Determination (R 2). The results obtained from these models were compared with the measured data.

Keywords

  • Support vector regression
  • Global horizontal irradiance
  • Combining forecasts
  • Algeria

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References

  1. Ming, T., Liu, W., Caillol, S.: Fighting global warming by climate engineering: is the Earth radiation management and the solar radiation management any option for fighting climate change? Renew. Sustain. Energy Rev. 31, 792–834 (2014)

    CrossRef  Google Scholar 

  2. Himri, Y., BoudgheneStambouli, A., Draoui, B., Himri, S.: Review of wind energy use in Algeria. Renew. Sustain. Energy Rev. 13, 910–914 (2009)

    CrossRef  Google Scholar 

  3. Bouzgou, H.: A fast and accurate model for forecasting wind speed and solar radiation time series based on extreme learning machines and principal components analysis. J. Renew. Sustain. Energy 6(1), 013114 (2014)

    CrossRef  Google Scholar 

  4. Chakhchoukh, Y., Panciatici, P., Mili, L.: Electric Load Forecasting Based on Statistical Robust Methods. IEEE Trans. Power Syst. 26, 982–991 (2011)

    CrossRef  Google Scholar 

  5. Wu, G., Liu, Y., Wang, T.J.: Methods and strategy for modeling daily global solar radiation with measured meteorological data: a case study in Nanchang station, China. Energy convers. manage. 48, 2447–2452 (2007)

    CrossRef  Google Scholar 

  6. Sen, Z.: Simple nonlinear solar irradiation estimation model. Renew. Energy 32, 342–350 (2007)

    CrossRef  Google Scholar 

  7. Salmi, M., Bouzgou, H., Al-Douri, Y., Boursas, A.: Evaluation of the hourly global solar radiation on a horizontal plane for two sites in Algeria. Adv. Mater. Res. 925, 641–645 (2014). Trans Tech Publications

    CrossRef  Google Scholar 

  8. Stambouli, A.B., Khiat, Z., Flazi, S., Kitamura, Y.: A review on the renewable energy development in Algeria: current perspective, energy scenario and sustainability issues. Renew. Sustain. Energy Rev. 16(7), 4445–4460 (2012)

    CrossRef  Google Scholar 

  9. Vapnik, V.N., Vapnik, V.: Statistical Learning Theory, vol. 1. Wiley, New York (1998)

    MATH  Google Scholar 

  10. Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)

    MathSciNet  CrossRef  Google Scholar 

  11. Scholköpf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)

    MATH  Google Scholar 

  12. Hibon, M., Evgeniou, T.: To combine or not to combine: selecting among forecasts and their combinations. Int. J. Forecast. 21(1), 15–24 (2005)

    CrossRef  Google Scholar 

  13. Bouzgou, H., Benoudjit, N.: Multiple architecture system for wind speed prediction. Appl. Energy 88(7), 2463–2471 (2011)

    CrossRef  Google Scholar 

  14. De Menezes, L.M., Bunn, D.W., Taylor, J.W.: Review of guidelines for the use of combined forecasts. Eur. J. Oper. Res. 120(1), 190–204 (2000)

    CrossRef  MATH  Google Scholar 

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Correspondence to Nahed Zemouri .

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Zemouri, N., Bouzgou, H. (2018). Ensemble of Support Vector Methods to Estimate Global Solar Radiation in Algeria. 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_16

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  • DOI: https://doi.org/10.1007/978-3-319-73192-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73191-9

  • Online ISBN: 978-3-319-73192-6

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