A new hybrid model for power forecasting of a wind farm using spatial–temporal correlations

Songkang Wen, Yanting Li and Yan Su

Renewable Energy, 2022, vol. 198, issue C, 155-168

Abstract: This paper develops a new hybrid forecasting model for the hourly power output of multiple turbines (or plants), aimed at making use of both the spatial and temporal correlations of weather factors and power outputs from wind turbines (or plants). To account for the time-varying nature of local pattern and wind propagation, a new clustering algorithm named Kmeans–Hierarchical Clustering (KHC) is first used to adaptively cluster the wind turbines (or plants). Then, singular value decomposition (SVD) is employed to extract the leading components of the wind power of the wind turbines (or plants) in the same cluster. Finally, the support vector regression (SVR) models are built for the leading components and the predictions of the components are transformed into the predictions of the power output of each turbine (or plant) with the inverse SVD. The comparison results show that the proposed model is able to improve the forecast accuracy over the existing short-term forecasting models.

Keywords: Local pattern; Wind propagation; Adaptively clustering; Support vector regression (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148122012101
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:155-168

DOI: 10.1016/j.renene.2022.08.044

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 ().