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A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting

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  • Aly, Hamed H.H.

Abstract

Wind energy is playing a compromising role in the new generation of sustainable energy and promising to increase more. Forecasting of the fluctuated wind speed and its output power is playing an essential role in the smart power system grid. The wind power integration is based on the accuracy of the wind speed and power forecasting models. This paper is proposing highly accurate hybrid deep learning clustered models for wind speed and power forecasting using different artificial intelligent systems for optimal performance. Various combinations of Recurrent Kalman Filter (RKF), Fourier Series (FS), Wavelet (WNN) and Artificial Neural Network (ANN) are used in this work. Twelve different hybrid models are proposed and tested. The novelty of this work is the applied clustered segments with the deep learning hybrid models to improve the aggregated system performance. This work is validated by using different unseen data set with the proposed models as well as using K-fold cross validation method. All the proposed models are performing well with high accurate results, but the hybrid clustered model of WNN and RKF outperforms all other models.

Suggested Citation

  • Aly, Hamed H.H., 2020. "A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting," Energy, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220318806
    DOI: 10.1016/j.energy.2020.118773
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Zhao, Xinyu & Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Yu, Daren & Chang, Juntao, 2021. "Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation," Energy, Elsevier, vol. 234(C).
    3. 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).
    4. Chengcheng Gu & Hua Li, 2022. "Review on Deep Learning Research and Applications in Wind and Wave Energy," Energies, MDPI, vol. 15(4), pages 1-19, February.
    5. Hu, Weicheng & Yang, Qingshan & Chen, Hua-Peng & Yuan, Ziting & Li, Chen & Shao, Shuai & Zhang, Jian, 2021. "New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory," Renewable Energy, Elsevier, vol. 179(C), pages 2174-2186.
    6. Hao Zhen & Dongxiao Niu & Min Yu & Keke Wang & Yi Liang & Xiaomin Xu, 2020. "A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
    7. Wang, Shuai & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2021. "A novel combined model for wind speed prediction – Combination of linear model, shallow neural networks, and deep learning approaches," Energy, Elsevier, vol. 234(C).
    8. Si, Jicang & Wang, Guochang & Li, Pengfei & Mi, Jianchun, 2021. "A new skeletal mechanism for simulating MILD combustion optimized using Artificial Neural Network," Energy, Elsevier, vol. 237(C).
    9. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
    10. Zhihao Shang & Quan Wen & Yanhua Chen & Bing Zhou & Mingliang Xu, 2022. "Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion," Energies, MDPI, vol. 15(8), pages 1-23, April.
    11. Tsao, Hao-Han & Leu, Yih-Guang & Chou, Li-Fen, 2021. "A center-of-concentrated-based prediction interval for wind power forecasting," Energy, Elsevier, vol. 237(C).
    12. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.

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