A Novel Short-Term Wind Speed Prediction Based on MFEC

Abstract

Accurate forecasting of short-term wind speed is a key technology to enable efficient and reliable operation of microgrids with wind generators. Results of wind speed prediction (WSP) methods in the current literature are subject to errors due to the random nature of wind speed and the limited generalization of forecast algorithms. In this paper, a short-term WSP method based on a model of forecast, error correction, wind power generation, support vector machines (SVMs), and prediction algorithms is proposed. In the proposed method, the error model is built to predict the errors of the original predicted wind speed. Subsequently, the predicted errors are incorporated into the original predicted sequence to produce the final forecasts. The final predicted results can be brought to the system operator step by step for use in scheduling strategies. Using SVM and back propagation (BP) prediction algorithm as examples for the basic prediction algorithm, the proposed method is applied to a day-ahead WSP with five daily deliveries and compared with the results by a single SVM or a BP prediction algorithm. The test results demonstrate significant improvement in prediction accuracy using the proposed short-term WSP based on a model of forecast error correction method, independent of the basic prediction algorithm.