@inproceedings{ad8540f038c7461cbaea284927e1f846,
title = "A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting",
abstract = "Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, pressure, dew point and solar radiation to accurately predict wind speeds. The prediction performance is extensively assessed using real data collected in West Texas, USA. The experimental results show that the proposed MSLSTM can preferably capture and learn uncertainties while output competitive performance.",
keywords = "Deep learning, LSTM, Stacked LSTMs, Wind speed prediction",
author = "Sisheng Liang and Long Nguyen and Fang Jin",
note = "Funding Information: This work was supported by the U.S. National Science Foundation under Grant CNS-1737634. The authors would like to thank West Texas Mesonet, Dr. John Schroeder and Mr. Wes Burget for providing weather data in this paper. Funding Information: ACKNOWLEDGMENT This work was supported by the U.S. National Science Foundation under Grant CNS-1737634. The authors would like to thank West Texas Mesonet, Dr. John Schroeder and Mr. Wes Burget for providing weather data in this paper. Publisher Copyright: {\textcopyright} 2018 IEEE.; null ; Conference date: 10-12-2018 Through 13-12-2018",
year = "2019",
month = jan,
day = "22",
doi = "10.1109/BigData.2018.8622332",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4561--4564",
editor = "Yang Song and Bing Liu and Kisung Lee and Naoki Abe and Calton Pu and Mu Qiao and Nesreen Ahmed and Donald Kossmann and Jeffrey Saltz and Jiliang Tang and Jingrui He and Huan Liu and Xiaohua Hu",
booktitle = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
}