Wind speed forecasting with correlation network pruning and augmentation: A two-phase deep learning method

Yang Yang, Jin Lang, Jian Wu, Yanyan Zhang, Lijie Su and Xiangman Song

Renewable Energy, 2022, vol. 198, issue C, 267-282

Abstract: To ensure the operational reliability of power systems, it is important for wind speed signal forecasting systems of wind turbines to be efficient, accurate and stable. This paper proposes a two-phase deep learning structure with network augmentation and pruning. By introducing the cross-correlation and quasi-convex optimization, a fractional quadratic programming problem and related convex optimization models are constructed to generate the augmented data for this proposed internal network; by pruning weakly correlated convolution channels, the redundant features of its external network are reduced. Furthermore, the closed-form solution of the convex optimization model is derived, which reduces the computational complexity considerably from O(n*log(2N)) to O(n). The proposed approach has been extensively validated using the real data of the wind farm in China. The results of the numerical experiments demonstrate that the proposed method achieves the superior performance in the training flexibility, model accuracy, stability, and interpretability.

Keywords: Two-phase deep learning method; Wind speed prediction; Cross-correlation function and correlation network; Network pruning and network augmentation; Fractional quadratic optimization (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148122011351
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:renene:v:198:y:2022:i:c:p:267-282

DOI: 10.1016/j.renene.2022.07.125

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

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