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A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system

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  • Liu, Jinqiang
  • Wang, Xiaoru
  • Lu, Yun

Abstract

With the increased penetration of wind power into the electric grid of China, many challenges emerge due to its fluctuation and intermittence. In this context, it is crucial to achieve higher accuracy of the short-term wind power forecasting for safe and economical operation of the power system. Hence, this paper proposes a novel hybrid methodology for short-term wind power forecasting, successfully combining three individual forecasting models using the adaptive neuro-fuzzy inference system (ANFIS). The backpropagation neural network (BPNN), radial basis function neural network (RBFNN), and least squares support vector machine (LSSVM) are selected as the individual forecasting models. A new data preprocessing method based on Pearson correlation coefficient (PCC) is also applied for selecting proper inputs for three individual models. Results obtained show the advancement of the PCC based data preprocessing method. Also, the comparison studies demonstrate that the proposed hybrid methodology presents a significant improvement in accuracy with respect to three individual models.

Suggested Citation

  • Liu, Jinqiang & Wang, Xiaoru & Lu, Yun, 2017. "A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system," Renewable Energy, Elsevier, vol. 103(C), pages 620-629.
    • Handle: RePEc:eee:renene:v:103:y:2017:i:c:p:620-629
    DOI: 10.1016/j.renene.2016.10.074
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    References listed on IDEAS

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    1. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    2. Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo & Monteiro, Cláudio & Sousa, João & Bessa, Ricardo, 2009. "Comparison of two new short-term wind-power forecasting systems," Renewable Energy, Elsevier, vol. 34(7), pages 1848-1854.
    3. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    4. 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.
    5. Peng, Huaiwu & Liu, Fangrui & Yang, Xiaofeng, 2013. "A hybrid strategy of short term wind power prediction," Renewable Energy, Elsevier, vol. 50(C), pages 590-595.
    6. Costa, Alexandre & Crespo, Antonio & Navarro, Jorge & Lizcano, Gil & Madsen, Henrik & Feitosa, Everaldo, 2008. "A review on the young history of the wind power short-term prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1725-1744, August.
    7. 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.
    8. Xiao, Ling & Wang, Jianzhou & Dong, Yao & Wu, Jie, 2015. "Combined forecasting models for wind energy forecasting: A case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 271-288.
    9. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    10. Wen-Yeau Chang, 2013. "Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method," Energies, MDPI, vol. 6(9), pages 1-18, September.
    11. Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
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