Wind power forecast based on convolutional neural network with multi-feature fusion

As a renewable clean energy, wind energy has the characteristics of large storage and wide distribution. It is one of the important components of energy internet. However, its strong fluctuation, randomness and discontinuity affect the stable operation of power system. To reduce the impact of wind p...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-11, Vol.1684 (1), p.12134
Main Authors: Huang, Qiyue, Wang, Hao
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Clean energy
Feature extraction
Mathematical models
Network reliability
Neural networks
Physics
Prediction models
Waveforms
Wind power
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Summary:As a renewable clean energy, wind energy has the characteristics of large storage and wide distribution. It is one of the important components of energy internet. However, its strong fluctuation, randomness and discontinuity affect the stable operation of power system. To reduce the impact of wind power on power network and improve the reliability of power prediction, a wind power forecast method based on convolutional neural network with multi-feature fusion is proposed. Firstly, the wind power is classified according to the change characteristic of the waveform. The feature of the wind power waveform is extracted by the convolution neural network (CNN). Then, a prediction model based on multi-feature fusion algorithm is established to accurately predict wind power. Finally, the corresponding simulation model is established to verify that the proposed method can effectively improve the reliability of wind power prediction.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1684/1/012134