Comparison of BLSTM-Attention and BLSTM-Transformer Models for Wind Speed Prediction

Abstract


 
 
 Accurate estimation of wind speed is essential for many meteorological applications. A novel short-term wind speed prediction method of Bi-directional LSTM and Transformer Network (BLSTM-TRA) model is proposed by combining the Transformer model and LSTM model, and a hybrid model of Bi-directional Long Short-term Memory and Attention Network (BLSTM-ATT) is proposed based on Attention mechanism and LSTM model. The proposed BLSTM-ATT and BLSTM-TRA model are used for predicting the wind speed of seven meteorological stations in Qingdao. In combination with historical ob- servation data, the proposed models outperform the Numerical Weather Prediction (NWP) system of European Centre for Medium-Range Weather Forecasts (ECMWF). By comparing the results of BLSTM-ATT, BLSTM-TRA and ECMWF forecast model, RMSE and MAE of BLSTM-ATT are reduced by 44.7% and 50.3% on average, respectively, as well as an average decrease of 43.0% in the RMSE, an average decrease of 47.4% in the MAE of the BLSTM-TRA model. This demonstrates that the BLSTM-ATT model and the BLSTM-TRA model are more accurate than the ECMWF model in wind speed prediction.