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Solar energy prediction using linear and non-linear regularization models: A study on AMS (American Meteorological Society) 2013–14 Solar Energy Prediction Contest

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  • Aggarwal, S.K.
  • Saini, L.M.

Abstract

In 2013, American Meteorological Society Committees on AI (artificial intelligence) Applications organized a short-term solar energy prediction competition aiming at predicting total daily solar energy received at 98 solar farms based on the outputs of various weather patterns of a numerical weather prediction model. In this paper, a methodology to solve this problem has been explained and the performance of ordinary LSR (least-square regression), regularized LSR and ANN (artificial neural network) models has been compared. In order to improve the generalization capability of the models, more experiments like variable segmentation, subspace feature sampling and ensembling of models have been conducted. It is observed that model accuracy can be improved by proper selection of input data segments. Further improvements can be obtained by ensemble of forecasts of different models. It is observed that the performance of an ensemble of ANN and LSR models is the best among all the proposed models in this work. As far as the competition is concerned, Gradient Boosting Regression Tree has turned out to be the best algorithm. The proposed ensemble of ANN and LSR model is able to show a relative improvement of 7.63% and 39.99% as compared to benchmark Spline Interpolation and Gaussian Mixture Model respectively.

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  • Aggarwal, S.K. & Saini, L.M., 2014. "Solar energy prediction using linear and non-linear regularization models: A study on AMS (American Meteorological Society) 2013–14 Solar Energy Prediction Contest," Energy, Elsevier, vol. 78(C), pages 247-256.
    • Handle: RePEc:eee:energy:v:78:y:2014:i:c:p:247-256
    DOI: 10.1016/j.energy.2014.10.012
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    1. Khatib, Tamer & Mohamed, Azah & Sopian, K., 2012. "A review of solar energy modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2864-2869.
    2. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
    3. Long, Huan & Zhang, Zijun & Su, Yan, 2014. "Analysis of daily solar power prediction with data-driven approaches," Applied Energy, Elsevier, vol. 126(C), pages 29-37.
    4. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2014. "Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 509-519.
    5. Rizwan, M. & Jamil, Majid & Kirmani, Sheeraz & Kothari, D.P., 2014. "Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters," Energy, Elsevier, vol. 70(C), pages 685-691.
    6. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    7. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    9. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
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    11. Fouilloy, Alexis & Voyant, Cyril & Notton, Gilles & Motte, Fabrice & Paoli, Christophe & Nivet, Marie-Laure & Guillot, Emmanuel & Duchaud, Jean-Laurent, 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability," Energy, Elsevier, vol. 165(PA), pages 620-629.
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