High and low frequency wind power prediction based on Transformer and BiGRU-Attention

Wang, Shuangxin; Shi, Jiarong; Yang, Wei; Yin, Qingyan

doi:10.1016/j.energy.2023.129753

Cited by 10 publications

(1 citation statement)

References 43 publications

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“…A substantial body of literature corroborates that the EFM outperforms the individual models in the context of WSP performance [25,26]. Taking the feature decomposition for example, Hu et al [27] and Wang et al [28] both employ ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for wind speed forecasting.…”

Section: Introductionmentioning

confidence: 96%

Short-Term Probabilistic Wind Speed Predictions Integrating Multivariate Linear Regression and Generative Adversarial Network Methods

Dong,

Li,

Shi

et al. 2024

Atmosphere

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The precise forecasting of wind speeds is critical to lessen the harmful impacts of wind fluctuations on power networks and aid in merging wind energy into the grid system. However, prior research has predominantly focused on point forecasts, often overlooking the uncertainties inherent in the prediction accuracy. For this research, we suggest a new approach for forecasting wind speed intervals (PI). Specifically, the actual wind speed series are initially procured, and the complete ensemble empirical mode decomposition coupled with adaptive noise (CEEMDAN) method decomposes the actual wind speed series into constituent numerous mode functions. Furthermore, a generative adversarial network (GAN) is utilized to achieve the wind speed PI in conjunction with the multivariate linear regression method. To confirm the effectiveness of the suggested model, four datasets are selected. The validation results suggest that this suggested model attains a superior PI accuracy compared with those of numerous benchmark techniques. In the context of PI of dataset 4, the PINAW values show improvements of 68.06% and 32.35% over the CEEMDAN-CNN and VMD-GRU values in single-step forecasting, respectively. In conclusion, the proposed model excels over the counterpart models by exhibiting diminished a PINAW and CWC, while maintaining a similar PICP.

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