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Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting

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  • Krishna Rayi, Vijaya
  • Mishra, S.P.
  • Naik, Jyotirmayee
  • Dash, P.K.

Abstract

In this paper, an efficient new hybrid time series forecasting model combining variational mode decomposition (VMD) and Deep learning mixed Kernel ELM (MKELM) Autoencoder (AE) has been presented for precise prediction of wind power. It is well known that KELM-AE avoids manual tuning of weights of hidden layer nodes, and exhibits superb model generalization property, reduces execution time, and produces exact solution of output weights by generalized least squares resulting in compact storage space, etc. This is in contrast to other deep learning neural network models with nonconvex optimization problems, time consuming training process and limitations of backpropagation learning algorithms. Further the VMD parameters (α, K) are optimized to yield suitable number of intrinsic modes (IMFs) using a newly presented meta-heuristic population based Sine Cosine integrated water cycle algorithm (SCWCA) algorithm. Further the mixed kernel parameters and their associated weights are optimized using the same SCWCA to improve the performance of the Deep MKELM predictor against any outliers or noise in the data. Wind power data of three wind farms located in Sotavento Spain, Wyoming, and California, USA are applied for short-term and multi-step prediction of wind power with significant accuracy.

Suggested Citation

  • Krishna Rayi, Vijaya & Mishra, S.P. & Naik, Jyotirmayee & Dash, P.K., 2022. "Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting," Energy, Elsevier, vol. 244(PA).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221028346
    DOI: 10.1016/j.energy.2021.122585
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    14. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).
    15. Meng, Anbo & Xie, Zhifeng & Luo, Jianqiang & Zeng, Ying & Xu, Xuancong & Li, Yidian & Wu, Zhenbo & Zhang, Zhan & Zhu, Jianbin & Xian, Zikang & Li, Chen & Yan, Baiping & Yin, Hao, 2023. "An adaptive variational mode decomposition for wind power prediction using convolutional block attention deep learning network," Energy, Elsevier, vol. 282(C).
    16. Jinhua Zhang & Hui Li & Peng Cheng & Jie Yan, 2024. "Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network," Energies, MDPI, vol. 17(2), pages 1-16, January.
    17. Wang, Hao & Ye, Jingzhen & Huang, Linxuan & Wang, Qiang & Zhang, Haohua, 2023. "A multivariable hybrid prediction model of offshore wind power based on multi-stage optimization and reconstruction prediction," Energy, Elsevier, vol. 262(PA).
    18. Yang, Zihao & Dong, Sheng, 2023. "A novel decomposition-based approach for non-stationary hub-height wind speed modelling," Energy, Elsevier, vol. 283(C).
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    20. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2023. "Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework," Energy, Elsevier, vol. 278(PA).

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