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Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method.
- Source :
-
Energies (19961073) . Apr2018, Vol. 11 Issue 4, p854. 1p. 1 Black and White Photograph, 1 Diagram, 1 Chart, 11 Graphs. - Publication Year :
- 2018
-
Abstract
- To meet the increasing wind power forecasting (WPF) demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD) pre-calculated flow fields (CPFF)-based WPF is a promising physical approach, which can balance well the competing demands of computational efficiency and accuracy. To enhance its adaptability for wind farms in complex terrain, a WPF method combining wind turbine clustering with CPFF is first proposed where the wind turbines in the wind farm are clustered and a forecasting is undertaken for each cluster. K-means, hierarchical agglomerative and spectral analysis methods are used to establish the wind turbine clustering models. The Silhouette Coefficient, Calinski-Harabaz index and within-between index are proposed as criteria to evaluate the effectiveness of the established clustering models. Based on different clustering methods and schemes, various clustering databases are built for clustering pre-calculated CFD (CPCC)-based short-term WPF. For the wind farm case studied, clustering evaluation criteria show that hierarchical agglomerative clustering has reasonable results, spectral clustering is better and K-means gives the best performance. The WPF results produced by different clustering databases also prove the effectiveness of the three evaluation criteria in turn. The newly developed CPCC model has a much higher WPF accuracy than the CPFF model without using clustering techniques, both on temporal and spatial scales. The research provides supports for both the development and improvement of short-term physical WPF systems. [ABSTRACT FROM AUTHOR]
- Subjects :
- *WIND power
*FLUID dynamics
*CLEAN energy
*DYNAMICAL systems
*TURBINES
Subjects
Details
- Language :
- English
- ISSN :
- 19961073
- Volume :
- 11
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Energies (19961073)
- Publication Type :
- Academic Journal
- Accession number :
- 129368090
- Full Text :
- https://doi.org/10.3390/en11040854