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

Authors

  • Zhifeng Liu Qingdao Meteorological Bureau
  • Feng Ding Qingdao Meteorological Bureau
  • Jianyong Lu Nanjing University of Information Science and Technology
  • Yue Zhou Nanjing University of Information Science and Technology
  • Hetao Chu Qingdao Meteorological Bureau Qingdao

DOI:

https://doi.org/10.7546/CRABS.2022.01.10

Keywords:

attention mechanism, transformer model, LSTM, wind speed forecasting

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.

Author Biographies

Zhifeng Liu, Qingdao Meteorological Bureau

Qingdao Meteorological Bureau
Qingdao 266003, China
lzflyjk@163.com

Feng Ding, Qingdao Meteorological Bureau

Qingdao Meteorological Bureau
Qingdao 266003, China
fdingqd@126.com

Jianyong Lu, Nanjing University of Information Science and Technology

Nanjing University of Information Science and Technology
Nanjing 210044, China
jylu@nuist.edu.cn

Yue Zhou, Nanjing University of Information Science and Technology

Nanjing University of Information Science and Technology
Nanjing 210044, China
yuezhou@nuist.edu.cn

Hetao Chu, Qingdao Meteorological Bureau Qingdao

Qingdao Meteorological Bureau Qingdao
266003, China
chuwang90@sina.com

 

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Published

02-02-2022

How to Cite

[1]
L. Zhifeng, D. Feng, L. Jianyong, Z. Yue, and C. Hetao, “Comparison of BLSTM-Attention and BLSTM-Transformer Models for Wind Speed Prediction”, C. R. Acad. Bulg. Sci. , vol. 75, no. 1, pp. 80–89, Feb. 2022.

Issue

Section

Geophysics