A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information

Mao Yang, Chao Han, Wei Zhang and Bo Wang

Energy, 2024, vol. 294, issue C

Abstract: In recent years, the installed capacity of wind power has rapidly increased. And the wind power prediction is the foundation for ensuring large-scale wind power grid connection. The current short-term prediction methods of wind farm cluster (WFC) are difficult to sufficiently extract spatiotemporal features to achieve high-precision prediction. The article proposes a short-term power prediction method for WFC based on deep attention embedded graph clustering-TimesNet (DAEGC-TimesNet). Firstly, the directed power curves of WFC are proposed to analyze wind data. Then, a graph attention network is constructed based on geographic location and numerical weather prediction (NWP) information to guide clustering algorithms to achieve effective cluster partitioning. Finally, the input of model is constructed based on the feature information from various sub-clusters of WFC and the prediction result is obtained through the TimesNet. The method is applied to WFCs in Inner Mongolia, Jilin province and Yunnan province of China. The result shows that the RMSE reduces 0.0155 and the MAE reduces 0.0156 as well as the coefficient of determination and accuracy rate are highest comparing with comparative algorithms averagely based on above three WFCs. The simulation results are superior to the comparison algorithms, which makes greater contributions to ensure regional power supply.

Keywords: Wind power prediction; Graph attention network; Cluster partitioning; Deep learning (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/S0360544224005425
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:s0360544224005425

DOI: 10.1016/j.energy.2024.130770

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 ().