SHORT-TERM WIND POWER PREDICTION BASED ON SAM-WGAN-GP

Huang Ling; Li Linxia; Cheng Yu; Xu Zijian

doi:10.19912/j.0254-0096.tynxb.2022-0265

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (4) : 180-188. DOI: 10.19912/j.0254-0096.tynxb.2022-0265

SHORT-TERM WIND POWER PREDICTION BASED ON SAM-WGAN-GP

Huang Ling^1-3, Li Linxia¹, Cheng Yu¹, Xu Zijian¹

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Abstract

Aiming at the problem of low prediction accuracy and unstable model of wind power prediction, a short-term wind power prediction model based on dual-stage self-attention mechanism wasserstein generative adversarial networks with gradient penalty （SAM-WGAN-GP） was proposed in this paper. Firstly, self-attention mechanism and time attention mechanism are introduced in the generative model of generative adversarial network to construct a dual-stage attention-based wasserstein generative adversarial networks with Gradient Penalty, the self-attention mechanism is brought into the generator to adaptively select the input features, a time attention mechanism is brought into the generator to capture the long-term dependence of wind power data time series; convolutional neural network is selected as the discriminator to improve the prediction accuracy of the model. Secondly, the generator loss function and root mean square error of SAM-WGAN-GP network are combined as the objective function to improve the stability of the model. Meanwhile, in order to solve the problem of slow learning of discriminators, TTUR is introduced to balance the training process of the network. Finally, the actual operation data of a wind farm in Jiuquan, Gansu Province are used as an example to verify that the SAM-WGAN-GP model. The results indicate that the model can not only adaptively select input features, but also capture the long time dependence of wind power date and improve the prediction accuracy.

Key words

wind power prediction / generating adversarial network / attentional mechanism / convolutional neural network

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Huang Ling, Li Linxia, Cheng Yu, Xu Zijian. SHORT-TERM WIND POWER PREDICTION BASED ON SAM-WGAN-GP[J]. Acta Energiae Solaris Sinica. 2023, 44(4): 180-188 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0265

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