  Short-term probabilistic wind power forecasting can provide critical
quantified uncertainty information of wind generation for power system
operation and control. As the complicated characteristics of wind power
prediction error, it would be difficult to develop a universal forecasting
model dominating over other alternative models. Therefore, a novel multi-model
combination (MMC) approach for short-term probabilistic wind generation
forecasting is proposed in this paper to exploit the advantages of different
forecasting models. The proposed approach can combine different forecasting
models those provide different kinds of probability density functions to
improve the probabilistic forecast accuracy. Three probabilistic forecasting
models based on the sparse Bayesian learning, kernel density estimation and
beta distribution fitting are used to form the combined model. The parameters
of the MMC model are solved based on Bayesian framework. Numerical tests
illustrate the effectiveness of the proposed MMC approach.
