Probabilistic Wind Power Forecasting Using Radial Basis Function Neural Networks

作者：George Sideratos, Nikos Hatziargyriou · 发表于：IEEE Transactions on Power Systems · 年份：2012 · DOI：10.1109/tpwrs.2012.2187803 · 被引用次数：238 · 研究领域：Energy Load and Power Forecasting、Image and Signal Denoising Methods、Neural Networks and Applications

A novel methodology for probabilistic wind power forecasting is described. The method is based on artificial intelligence and concentrates on the uncertainty information about the future wind power production predicting a set of quantiles with predefined nominal probabilities. The proposed model uses the point predictions of an existing state-of-the-art wind power forecasting model and forecasts the prediction uncertainties due to the inaccuracies of the numerical weather predictions (NWP), the weather stability and the deterministic forecasting model. The performance of the proposed model is evaluated on two wind farms that are located in areas with different weather conditions.