Multi-dimensional scenario forecast for generation of multiple wind farms

A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed. In the proposed approach, support vector machine (SVM) is applied for the spot forecast of wind power generation. The pro...

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Bibliographic Details
Published in:Journal of modern power systems and clean energy 2015-09, Vol.3 (3), p.361-370
Main Authors: YANG, Ming, LIN, You, ZHU, Simeng, HAN, Xueshan, WANG, Hongtao
Format: Article
Language:English
Subjects:
Dynamic conditional correlation matrix
Electrical Machines and Networks
Energy
Energy Systems
Gaussian copula
Multidimensional scenario forecast
Power Electronics
Renewable and Green Energy
Sparse Bayesian learning (SBL)
Support vector machine (SVM)
Wind power generation forecast
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Summary:A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed. In the proposed approach, support vector machine (SVM) is applied for the spot forecast of wind power generation. The probability density function (PDF) of the SVM forecast error is predicted by sparse Bayesian learning (SBL), and the spot forecast result is corrected according to the error expectation obtained. The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression (DCCMR) model to describe the correlation among the errors. And the multi-dimensional scenario is generated with respect to the estimated marginal distributions and the copula function. Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.
ISSN:2196-5625
2196-5420
DOI:10.1007/s40565-015-0110-6