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Article

A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network

1
School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
2
Numerical Weather Prediction Center, CMA, Beijing 100081, China
3
School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Sunil Kumar Jha, Xiaorui Zhang, Limao Zhang and Nilesh Patel
Received: 22 March 2021 / Revised: 12 May 2021 / Accepted: 16 May 2021 / Published: 19 May 2021
Accurately forecasting wind speed on a short-term scale has become essential in the field of wind power energy. In this paper, a multi-variable long short-term memory network model (MV-LSTM) based on Pearson correlation coefficient feature selection is proposed to predict the short-term wind speed. The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, and air pressure, to predict the wind speed in the next hour. Hourly data collected from two ground observation stations in Yanqing and Zhaitang in Beijing were divided into training and test sets. The training sets were used to train the model, and the test sets were used to evaluate the model with the root-mean-square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE) metrics. The proposed method is compared with two other forecasting methods (the autoregressive moving average model (ARMA) method and the single-variable long short-term memory network (LSTM) method, which inputs only historical wind speed data) based on the same dataset. The experimental results prove the feasibility of the MV-LSTM method for short-term wind speed forecasting and its superiority to the ARMA method and the single-variable LSTM method. View Full-Text
Keywords: wind speed prediction; multi-variable; LSTM; neural networks wind speed prediction; multi-variable; LSTM; neural networks
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MDPI and ACS Style

Xie, A.; Yang, H.; Chen, J.; Sheng, L.; Zhang, Q. A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network. Atmosphere 2021, 12, 651. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12050651

AMA Style

Xie A, Yang H, Chen J, Sheng L, Zhang Q. A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network. Atmosphere. 2021; 12(5):651. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12050651

Chicago/Turabian Style

Xie, Anqi, Hao Yang, Jing Chen, Li Sheng, and Qian Zhang. 2021. "A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network" Atmosphere 12, no. 5: 651. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12050651

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