An ultra-short-term wind speed correction method based on the fluctuation characteristics of wind speed

Abstract

The WRF model cannot produce an accurate wind speed prediction to guarantee the steady operation of wind farms due to the inability of wind farms to acquire meteorological information and imperfections in physical schemes. This paper suggested an ultra-short-term wind speed correction method based on wind speed fluctuation characteristics, known as VSDA-BO-LSTM, in order to increase the accuracy of wind speed prediction from WRF model. First, the Volatile Stable Day model (VSDA), which separated the original wind speed data into segments, extracted fluctuation features, and categorized them, was proposed. This model successfully addressed the lack of common wind speed feature extraction and wind speed feature stripping. Then, the long-short-term memory (LSTM) deep learning algorithm combined with the output of the VSDA model was used to correct the wind speed prediction value of the WRF model. At the same time, the Bayesian optimization algorithm was used to determine the parameters of LSTM.The results of the experiment demonstrated the suggested method’s good capability for wind speed correction. Moreover, the proposed method was very competitive compared to the current state-of-the-art model.