Research on Application of Artificial Neural Network for Sea Surface Wind Speed Forecasting
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Abstract:
Accurate estimation of wind speed is essential for many meteorological applications. A novel shortterm wind speed prediction method of the BLSTM-TRA model is proposed by combining the Transformer model and LSTM model. Six stations along the southern coast of the Shandong Peninsula are selected as the research area. After comparing and analyzing the 6 h prediction results of the 2018 ECMWF model, the following conclusions are drawn: The BLSTM-TRA multi-step prediction model can reduce the error of wind speed prediction. Compared with the ECMWF prediction model results, the RMSE and MAE of BLSTM-TRA are decreased by 58.9% and 63.2% on average. The analysis of wind speed error and wind statistical process shows that the BLSTM-TRA model has a certain anti-interference ability and can capture the sensitive information of short-term wind, etc., which is obviously better than the ECWMF model and traditional LSTM model for wind prediction.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.