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A Hybrid Optimized Model of Adaptive Neuro-Fuzzy Inference System, Recurrent Kalman Filter and Neuro-Wavelet for Wind Power Forecasting Driven by DFIG

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  • Aly, Hamed H.H.

Abstract

Renewable energy resources are playing a compromising role in the new generation of sustainable energy and smart grid. Wind power is playing a crucial role these days to minimize the fossil fuel emissions. Their integration is depending on a highly accurate forecasting model due to its intermittency, nonlinearity, and fluctuation. This work presents a hybrid optimized model of Adaptive Neuro-Fuzzy Inference System (ANFIS), Recurrent Kalman Filter (RKF) and Neuro-Wavelet (WNN) for Wind Power Forecasting Driven by doubly fed induction generator (DFIG). The predictions of individual models and hybrid of ANFIS, RKF and WNN models for wind speed and power generated are compared with other published work results in the literature. Six different hybrid models are proposed (ANFIS+WNN+RKF, ANFIS+RKF+WNN, WNN+ANFIS+RKF, WNN+RKF+ANFIS, RKF+WNN+ANFIS, RKF+ANFIS+WNN). The results of this work indicate that all proposed hybrid models are performing well but the hybrid of ANFIS+RKF+WNN in sequence has the optimal performance compared to other models.

Suggested Citation

  • Aly, Hamed H.H., 2022. "A Hybrid Optimized Model of Adaptive Neuro-Fuzzy Inference System, Recurrent Kalman Filter and Neuro-Wavelet for Wind Power Forecasting Driven by DFIG," Energy, Elsevier, vol. 239(PE).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pe:s0360544221026165
    DOI: 10.1016/j.energy.2021.122367
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    References listed on IDEAS

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    1. Aly, Hamed H.H., 2020. "A novel approach for harmonic tidal currents constitutions forecasting using hybrid intelligent models based on clustering methodologies," Renewable Energy, Elsevier, vol. 147(P1), pages 1554-1564.
    2. Aly, Hamed H.H., 2020. "A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting," Energy, Elsevier, vol. 213(C).
    3. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
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