A novel offshore wind power prediction model based on TCN-DANet-sparse transformer and considering spatio-temporal coupling in multiple wind farms

Juntao Chen, Xueying Fu, Lingli Zhang, Haoye Shen and Jibo Wu

Energy, 2024, vol. 308, issue C

Abstract: Offshore wind power capacity is growing, leading to larger clustered farms. Accurately predicting offshore wind power capacity is crucial for power system stability; however, current studies often overlook neighbouring installations. To address this, this study presents the Temporal Convolutional Network-Dual Attention Network-Sparse Transformer (TCN-DANet-Sparse Transformer) model, which considers the spatiotemporal coupling of multiple wind farms. Before detailing our model, we review the existing prediction methods, noting their limitations in capturing interconnected adjacent wind farms. Our model integrates spatial information from nearby farms to enhance prediction reliability. Through Pearson Correlation Coefficient analysis, we explore the temporal and spatial coupling features. Using overlapping sliding windows, we partition farms into subsequences, processed with TCN-DANet for efficient spatio-temporal feature extraction. These features are then input into the Sparse Transformer to improve the computational efficiency. Validated using a dataset from Kächele et al., our model outperforms the baseline on the London Wind Farm. In spring, for Case 1, the mean square error (MSE) of the main model decreased by 43.19 % compared to that of the TCN-DANet-transformer model. Similarly, for Case 2, the MSE of the main model is reduced by 41.69 %.

Keywords: Dual attention network; Temporal convolutional network; Offshore wind power prediction; Sparse transformer; Spatio-temporal coupling (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224026732
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:308:y:2024:i:c:s0360544224026732

DOI: 10.1016/j.energy.2024.132899

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 ().