Error Analysis and Correction of Predicted Wind Speed in Wind Power Forecasting

With the characteristics of the wind power’s randomness,intermittence and uncontrollability,the wind power prediction is greatly significant for the reliability and economic operation of power system.The wind speed forecasting is the most important part during the wind power forecasting course.The method of wind speed prediction is divided into two ways: one is using numerical weather prediction while the other one is not.Based on the WRF mesoscale weather forecast model,the paper introduces its characteristics briefly and analyzes the error of wind speed of the forecasting.And then proposes the linear regression and BP neural network model to correct the prediction value of the wind speed.The effect of each correction model is obtained by validating and analyzing the actual data.The test results show that the proposed method improves the prediction accuracy of wind speed.