A novel model for ultra-short term wind power prediction based on Vision Transformer

Ling Xiang, Xiaomengting Fu, Qingtao Yao, Guopeng Zhu and Aijun Hu

Energy, 2024, vol. 294, issue C

Abstract: Wind power has quickly developed in the world owing to the advantages of pure, inexpensive, and inexhaustible. However, strong volatility, unmanageable, and randomness make it difficult to achieve secure wind power generation. An excellent wind power prediction is effective for power system scheduling and safely stable operation. Vision Transformer (ViT) model is introduced for building a connection of the extracted characteristics and desired output. Long-short term memory (LSTM) is combined with ViT, and a new wind power forecasting model is proposed in this paper. For the proposed LSTM-ViT model, the temporal aspects of the weather data and correspondence properties are extracted based on LSTM. The link of the output and characteristic is established in view of the ViT, and the multi-headed self-attentiveness mechanisms in ViT fully exploit the relationship between the inputs. The validity and sophistication of the LSTM-ViT method are validated by the climate statistics and statistics of wind power. The results indicate that the wind power forecasting model is provided with higher prediction accuracy. The forecast results for the fourth quarter are used as analysis cases. The root mean square error of the method is reduced by 41.77%, 16.60%, 28.72%, 26.81%, and 16.25% compared to gate recurrent unit (GRU), LSTM, ViT, convolutional neural network (CNN)-ViT, and GRU-ViT respectively. The mean absolute error of the LSTM-ViT method in the first quarter is 0.327, with model comparison values reduction of 33.71%, 38.30%, 32.99%, 17.63% and 10.65% respectively.

Keywords: Wind power prediction; Vision transformer; Temporal feature; Self-attention; Long-short term memory (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224006261
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006261

DOI: 10.1016/j.energy.2024.130854

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().