Short-Term Forecast and Error Analysis of Wind Power Based on Weight Factors

Abstract

For power grid with large-scale wind energy, the short-term wind power prediction is important to the grid’s scheduling and stable operating. The overall short-term forecast for wind power connected to the grid relies on the wind velocity and historical power data. Firstly, K-means clustering is introduced to model the power grid, so that the relationship between wind velocity and power can be perfectly described. Considering that there are multiple factors contributing to the prediction of wind velocity and power, we use real data of 15 wind generating set to obtain dependable weight factors of all those dimensions. With the support of mass data, the prediction of power is proved by several measurements (ME, MRE, MAE, RMSE) to be accurate.