The data-based adaptive graph learning network for analysis and prediction of offshore wind speed

Yuting Ren, Zhuolin Li, Lingyu Xu and Jie Yu

Energy, 2023, vol. 267, issue C

Abstract: Offshore wind power plays an important role in the economy because of its abundant resources and great potential. Therefore, predicting offshore wind power significantly affects the intelligent management of power generation. However, tackling such forecasting task usually meet huge challenges due to the complex-temporal dependence on offshore wind data. Recently, deep learning approaches have successfully demonstrated their ability in modeling time series data. However, they often have significant limitations for failing to explore dynamic spatio-temporal dependencies between signals. In this paper, we propose a new framework DAGLN, which performs spatial dependency modelling through data-driven graph construction and graph learning, breaking through the limitations of predefined graph structures to obtain high-dimensional spatial features and capturing temporal information from them based on GRU structure. The model can play a powerful role in mining spatio-temporal correlations in multi-node and multi-step wind speed data prediction. Extensive experiments on selected nodes and data in the China Sea show the developed approach can outperform state-of-art models in multi-node wind speed prediction.

Keywords: Intelligent prediction of offshore wind; Spatio-temporal dependence; Graph neural network; Adaptive graph learning (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222034776
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:267:y:2023:i:c:s0360544222034776

DOI: 10.1016/j.energy.2022.126590

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 ().