Short-term Wind Power Prediction based on Combined LSTM

Zhao, Zhao Yuyang; Li, Li Lincong; Guo, Guo Yingjun; Shi, Shi Boming; Sun, Sun Hexu

doi:10.22541/au.168607315.50841577/v1

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…In [18], it is based on the long short-term memory network (LSTM), deep learning optimization technology, variational mode decomposition (VMD) [19], an LSTM-based wind power interval prediction model is realized. In [20], it combines the high-quality data feature extraction ability of the convolutional neural network (CNN) with the ability of the LSTM to describe the time series and obtains a more accurate prediction model. In [21], it proposes an LSTM neural network based on the attention mechanism, enabling the model to pay more attention to important information in the time series.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Short-Term Wind Power Forecasting Based on CGAN-CNN-LSTM Model Supported by Lidar

Zhang

Zhao

Yan

et al. 2023

Sensors

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Accurate prediction of wind power is of great significance to the stable operation of the power system and the vigorous development of the wind power industry. In order to further improve the accuracy of ultra-short-term wind power forecasting, an ultra-short-term wind power forecasting method based on the CGAN-CNN-LSTM algorithm is proposed. Firstly, the conditional generative adversarial network (CGAN) is used to fill in the missing segments of the data set. Then, the convolutional neural network (CNN) is used to extract the eigenvalues of the data, combined with the long short-term memory network (LSTM) to jointly construct a feature extraction module, and add an attention mechanism after the LSTM to assign weights to features, accelerate model convergence, and construct an ultra-short-term wind power forecasting model combined with the CGAN-CNN-LSTM. Finally, the position and function of each sensor in the Sole du Moulin Vieux wind farm in France is introduced. Then, using the sensor observation data of the wind farm as a test set, the CGAN-CNN-LSTM model was compared with the CNN-LSTM, LSTM, and SVM to verify the feasibility. At the same time, in order to prove the universality of this model and the ability of the CGAN, the model of the CNN-LSTM combined with the linear interpolation method is used for a controlled experiment with a data set of a wind farm in China. The final test results prove that the CGAN-CNN-LSTM model is not only more accurate in prediction results, but also applicable to a wide range of regions and has good value for the development of wind power.

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Section: Introductionmentioning

confidence: 99%