Multi-dimensional scenario forecast for generation of multiple wind farms

Abstract

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.