A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting

Sisheng Liang; Long Nguyen; Fang Jin

doi:10.1109/BigData.2018.8622332

A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting

Sisheng Liang, Long Nguyen, Fang Jin

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

47 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
Editors	Naoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey Saltz
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4561-4564
Number of pages	4
ISBN (Electronic)	9781538650356
DOIs	https://doi.org/10.1109/BigData.2018.8622332
State	Published - Jul 2 2018
Event	2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States Duration: Dec 10 2018 → Dec 13 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference	2018 IEEE International Conference on Big Data, Big Data 2018
Country/Territory	United States
City	Seattle
Period	12/10/18 → 12/13/18

Keywords

Deep learning
LSTM
Stacked LSTMs
Wind speed prediction

Access to Document

10.1109/BigData.2018.8622332

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Liang, S., Nguyen, L., & Jin, F. (2018). A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, Y. Song, D. Kossmann, B. Liu, K. Lee, J. Tang, J. He, & J. Saltz (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 4561-4564). Article 8622332 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8622332

Liang, Sisheng ; Nguyen, Long ; Jin, Fang. / A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. editor / Naoki Abe ; Huan Liu ; Calton Pu ; Xiaohua Hu ; Nesreen Ahmed ; Mu Qiao ; Yang Song ; Donald Kossmann ; Bing Liu ; Kisung Lee ; Jiliang Tang ; Jingrui He ; Jeffrey Saltz. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 4561-4564 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

@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 = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Big Data, Big Data 2018 ; Conference date: 10-12-2018 Through 13-12-2018",

year = "2018",

month = jul,

day = "2",

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 = "Naoki Abe and Huan Liu and Calton Pu and Xiaohua Hu and Nesreen Ahmed and Mu Qiao and Yang Song and Donald Kossmann and Bing Liu and Kisung Lee and Jiliang Tang and Jingrui He and Jeffrey Saltz",

booktitle = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",

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Liang, S, Nguyen, L & Jin, F 2018, A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting. in N Abe, H Liu, C Pu, X Hu, N Ahmed, M Qiao, Y Song, D Kossmann, B Liu, K Lee, J Tang, J He & J Saltz (eds), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018., 8622332, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Institute of Electrical and Electronics Engineers Inc., pp. 4561-4564, 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, United States, 12/10/18. https://doi.org/10.1109/BigData.2018.8622332

A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting. / Liang, Sisheng; Nguyen, Long; Jin, Fang.
Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / Naoki Abe; Huan Liu; Calton Pu; Xiaohua Hu; Nesreen Ahmed; Mu Qiao; Yang Song; Donald Kossmann; Bing Liu; Kisung Lee; Jiliang Tang; Jingrui He; Jeffrey Saltz. Institute of Electrical and Electronics Engineers Inc., 2018. p. 4561-4564 8622332 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting

AU - Liang, Sisheng

AU - Nguyen, Long

AU - Jin, Fang

PY - 2018/7/2

Y1 - 2018/7/2

N2 - 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.

AB - 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.

KW - Deep learning

KW - LSTM

KW - Stacked LSTMs

KW - Wind speed prediction

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U2 - 10.1109/BigData.2018.8622332

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M3 - Conference contribution

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T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

SP - 4561

EP - 4564

BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

A2 - Abe, Naoki

A2 - Liu, Huan

A2 - Pu, Calton

A2 - Hu, Xiaohua

A2 - Ahmed, Nesreen

A2 - Qiao, Mu

A2 - Song, Yang

A2 - Kossmann, Donald

A2 - Liu, Bing

A2 - Lee, Kisung

A2 - Tang, Jiliang

A2 - He, Jingrui

A2 - Saltz, Jeffrey

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Big Data, Big Data 2018

Y2 - 10 December 2018 through 13 December 2018

ER -

Liang S, Nguyen L, Jin F. A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting. In Abe N, Liu H, Pu C, Hu X, Ahmed N, Qiao M, Song Y, Kossmann D, Liu B, Lee K, Tang J, He J, Saltz J, editors, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 4561-4564. 8622332. (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). doi: 10.1109/BigData.2018.8622332

A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting

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