NOVEL ULTRA-SHORT-TERM WIND POWER COMBINATION FORECASTING METHOD BASED ON BITCN-INFORMER

Yin Yupeng; Kuang Honghai; Cao Shipeng; Yang Huixian; Li Xingyu

doi:10.19912/j.0254-0096.tynxb.2025-0025

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (5) : 350-356. DOI: 10.19912/j.0254-0096.tynxb.2025-0025

NOVEL ULTRA-SHORT-TERM WIND POWER COMBINATION FORECASTING METHOD BASED ON BITCN-INFORMER

Yin Yupeng, Kuang Honghai, Cao Shipeng, Yang Huixian, Li Xingyu

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Abstract

Aiming at the problem that the prediction accuracy of fluctuation data intervals and abrupt data points near the local peak value of traditional wind power prediction methods is limited, an ultra-short term wind power prediction method based on BiTCN-Informer fusion of temporal and spatial characteristics is proposed. Firstly, bidirectional temporal convolutional network （BiTCN） is used to analyze the information features of past observations and future covariates, and to extract the spatial features of the data. Then, the Informer probabilistic sparse self-attention mechanism and self-attention distillation mechanism are used to simplify the network parameters, reduce the computational complexity, identify the dependency relationship of long sequence data, and capture the temporal features of the sequence. Finally, a fully connected layer is introduced to integrate temporal and spatial features. The results of the case study show that the proposed combined model can effectively improve the prediction accuracy compared with other models.

Key words

wind power forecasting / bidirectional temporal convolutional network / Informer / probsparse self-attention / self-attention distilling

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Yin Yupeng, Kuang Honghai, Cao Shipeng, Yang Huixian, Li Xingyu. NOVEL ULTRA-SHORT-TERM WIND POWER COMBINATION FORECASTING METHOD BASED ON BITCN-INFORMER[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 350-356 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0025

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