Wind Power Probabilistic Forecasting Based on Wind Correction Using Weather Research and Forecasting Model

This paper proposes a wind power probabilistic forecasting (WPPF) method by correcting the wind forecasting (WF) obtained from the weather research and forecasting (WRF) simulation. Short term wind power forecasting highly depends on the numerical weather prediction (NWP), wind forecasting especially, which contributes the most error of the predicted wind power. This method is based on the WRF, it first analyzes the performance on wind forecasting and the relationship between the predicted wind and the observed one. Second, the Hidden Markov Model (HMM) ameliorated by the Gaussian mixture model (GMM) is introduced to extract the association of the predicted wind and forecasting error and the inner temporal ship of error itself, and makes correction on the raw wind forecasting. Finally, the spare Bayesian learning (SBL) is used to generate the wind power probabilistic prediction. Case study demonstrates the proposed method significantly enhances wind forecasting accuracy and lead to a better WPPF.