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An improved neural network-based approach for short-term wind speed and power forecast

Author

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  • Chang, G.W.
  • Lu, H.J.
  • Chang, Y.R.
  • Lee, Y.D.

Abstract

Accurate forecasts of wind speed and wind power generation are essential for the effective operation of a wind farm. This paper presents an improved radial basis function neural network-based model with an error feedback scheme (IRBFNN-EF) for forecasting short-term wind speed and power of a wind farm, where an additional shape factor is included in the classic Gaussian basis function associated with each neuron in the hidden layer and a simple parameter initialization method is proposed to effectively find initial values of two key parameters of the basis function when performing neural network training. A wind farm near central Taiwan area connected to Taipower system is served as the measurement target. Provided with 24 h of input data at 10-min resolution (i.e. 6 × 24 input time steps) for training the proposed neural network, a look-ahead time up to 72 h (i.e. 6 × 72 forecasted output time steps) have been performed. Test cases for different months over 2014 are reported. Results obtained by the proposed model are compared with those obtained by four other artificial neural network-based forecasting methods. It shows that the proposed model leads to better accuracy for forecasting wind speed and wind power while the computational efficiency is maintained.

Suggested Citation

  • Chang, G.W. & Lu, H.J. & Chang, Y.R. & Lee, Y.D., 2017. "An improved neural network-based approach for short-term wind speed and power forecast," Renewable Energy, Elsevier, vol. 105(C), pages 301-311.
    • Handle: RePEc:eee:renene:v:105:y:2017:i:c:p:301-311
    DOI: 10.1016/j.renene.2016.12.071
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    References listed on IDEAS

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    1. Shi, Jing & Guo, Jinmei & Zheng, Songtao, 2012. "Evaluation of hybrid forecasting approaches for wind speed and power generation time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3471-3480.
    2. Morales, J.M. & Mínguez, R. & Conejo, A.J., 2010. "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, Elsevier, vol. 87(3), pages 843-855, March.
    3. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    4. Liu, Hui & Tian, Hongqi & Liang, Xifeng & Li, Yanfei, 2015. "New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 83(C), pages 1066-1075.
    5. Haque, Ashraf U. & Mandal, Paras & Kaye, Mary E. & Meng, Julian & Chang, Liuchen & Senjyu, Tomonobu, 2012. "A new strategy for predicting short-term wind speed using soft computing models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4563-4573.
    6. Troncoso, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Riquelme, J.C. & Prieto, L., 2015. "Local models-based regression trees for very short-term wind speed prediction," Renewable Energy, Elsevier, vol. 81(C), pages 589-598.
    7. Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
    8. Hibon, Michele & Evgeniou, Theodoros, 2005. "To combine or not to combine: selecting among forecasts and their combinations," International Journal of Forecasting, Elsevier, vol. 21(1), pages 15-24.
    9. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    10. Riahy, G.H. & Abedi, M., 2008. "Short term wind speed forecasting for wind turbine applications using linear prediction method," Renewable Energy, Elsevier, vol. 33(1), pages 35-41.
    11. Bouzgou, Hassen & Benoudjit, Nabil, 2011. "Multiple architecture system for wind speed prediction," Applied Energy, Elsevier, vol. 88(7), pages 2463-2471, July.
    12. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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