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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)


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.


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

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

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