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Short-term probabilistic wind power forecasting can provide critical quantified uncertainty information of wind generation for power system operation and control. It would be difficult to develop a universal forecasting model dominating over other alternative models because of the inherent stochastic nature of wind power. Therefore, a novel multi-model combination (MMC) approach for probabilistic wind power 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 performance of probabilistic forecasting. 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 by two-step optimization. Comprehensive numerical studies illustrate the effectiveness of the proposed MMC approach. [ABSTRACT FROM AUTHOR]

Wind generation forecast approaches are typically based on fixed model structures, which might fit well with existing data but fall short in forecasting out-of-sample data. The reason lies in that predetermined models cannot always capture the actual dynamic features of wind farms, resulting in unavoidable model errors. In this paper, a novel ultra-short-term wind generation forecast approach based on multivariate empirical dynamic modeling is proposed. The time series of wind generation and the explanatory variables are applied for attractor reconstruction according to Takens’ theorem. By this means, the dynamics of the original wind generation system can be accurately captured by the trajectories of the state variables in the reconstructed space. Then, using a simplex projection approach, wind generation can be directly forecasted with the trajectories. A unique feature of the proposed approach is that its effectiveness depends solely on the potential context hidden in the sequences of the state variables rather than the correctness of the predetermined models. Consequently, this approach can faithfully depict the dynamics of wind farms and improve the forecast accuracy. Case studies illustrate that the proposed approach can obtain advanced forecast results comparing with the benchmark methods. [ABSTRACT FROM AUTHOR]