Application of Ensemble Selection Method in Short-term Wind Power Forecasting
To improve the accuracy of short-term wind speed and power forecasts as well as reduce the impact of wind power uncertainty on the grid system,this study attempted to use wind speed observations to select the optimal numerical forecasting ensemble members that closely matched the actual wind speed within the forecast window.Those selected ensemble members then formed an optimized ensemble for training and testing machine learning models.Unlike the conventional method that relied solely on ensemble averaging,this method considered the forecast discrepancies among different ensemble members,avoided the introduction of members with large errors,and thus helped to improve wind speed prediction.The optimal number of ensemble members for wind farms with different altitudes and terrains in Henan and Gansu was determined based on the results of ensemble performance and sensitivity experiments.Comparative analyses demonstrated that the ensemble selection forecasts outperformed ensemble averaging in predicting wind speed fluctuations during different weather processes,closely aligning with actual wind speed observations.The sea-level pressure field estimates generated by the ensemble selection method exhibited a higher level of agreement with ERA5 data.Evaluation of wind speed and power prediction over eleven consecutive months in different wind farms showed that the ensemble selection method improved the accuracy of diurnal variation and monthly average wind speed compared to the original ensemble averaging method.Analysis of the observed data of upslope and downslope winds with different durations and speed changes in two wind farms showed that there were the most wind speed changes of 2-4 m s-1 within 0-2 h and 2-4 h.Compared to ensemble averaging,the ensemble selection method significantly improved the prediction accuracy for these upslope and downslope winds.Furthermore,using the machine learning algorithm to train the optimal selection ensemble can further reduce the absolute deviation and root mean square error of wind speed,thereby effectively improving the accuracy of power prediction.
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