A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network

Anbo Meng, Shu Chen, Zuhong Ou, Jianhua Xiao, Jianfeng Zhang, Shun Chen, Zheng Zhang, Ruduo Liang, Zhan Zhang, Zikang Xian, Chenen Wang, Hao Yin and Baiping Yan

Energy, 2022, vol. 261, issue PA

Abstract: The accuracy and stability of wind power forecasting are very important for the operation of wind farms. However, for the newly built wind farms without sufficient historical data, it is difficult to make a more accurate prediction. Therefore, it is of great significance to explore a method to improve the wind power prediction accuracy with no sufficient historical data available. In this paper, a novel prediction model is proposed to address the few-shot learning problem of wind power prediction in new-built wind farms based on secondary evolutionary generative adversarial networks (SEGAN) and dual-dimension attention mechanism (DDAM) assisted bidirectional gate recurrent unit (BiGRU). The SEGAN first introduces the secondary evolutionary learning paradigm into learning GAN, aiming to learn the marginal distribution of real data and generate high-quality realistic data to augment the training dataset. In the prediction stage, the DDAM is attempted to obtain a new input matrix with global weight allocation and improve the sensitivity of the BiGRU model to the key information of the input data. The proposed SEGAN-DDAM-BiGRU model is validated on the data from the Galicia Wind Farm in Sotavento and the experimental results show that the proposed model is applicative for short-term prediction of new-built wind farms.

Keywords: econdary evolutionary computation; Generative adversarial network; Dual-dimension attention mechanism; New-built wind farms; Few-shot learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:261:y:2022:i:pa:s0360544222021612

DOI: 10.1016/j.energy.2022.125276

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