Wind power generation prediction based on LSTM

Jinxia Zhang; Xuru Jiang; Xin Chen; Xiaojing Li; Dong Guo; Lixin Cui

doi:10.1145/3325730.3325735

Wind power generation prediction based on LSTM

Jinxia Zhang, Xuru Jiang, Xin Chen, Xiaojing Li, Dong Guo, Lixin Cui

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

21 Citations (Scopus)

Abstract

In recent years, with the increasing proportion of wind power generation, the impact of wind power generation on grid security is also growing. This makes the prediction accuracy of wind power generation higher and higher. This paper utilizes the LSTM model of the deep learning domain to predict wind power generation. Besides, Auto Encoder is employed to reduce the data dimension, improve the generalization ability of the model, and shorten the training time. Simulation experiments show that the LSTM model has better prediction accuracy than other machine learning model such as SVM.

Original language	English
Title of host publication	ICMAI 2019 - Proceedings of 2019 4th International Conference on Mathematics and Artificial Intelligence
Publisher	Association for Computing Machinery
Pages	85-89
Number of pages	5
ISBN (Electronic)	9781450362580
DOIs	https://doi.org/10.1145/3325730.3325735
Publication status	Published - 12 Apr 2019
Externally published	Yes
Event	4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019 - Chegndu, China Duration: 12 Apr 2019 → 15 Apr 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019
Country/Territory	China
City	Chegndu
Period	12/04/19 → 15/04/19

Keywords

Auto encoder
Deep learning
Long short-term memory

Access to Document

10.1145/3325730.3325735

Cite this

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title = "Wind power generation prediction based on LSTM",

abstract = "In recent years, with the increasing proportion of wind power generation, the impact of wind power generation on grid security is also growing. This makes the prediction accuracy of wind power generation higher and higher. This paper utilizes the LSTM model of the deep learning domain to predict wind power generation. Besides, Auto Encoder is employed to reduce the data dimension, improve the generalization ability of the model, and shorten the training time. Simulation experiments show that the LSTM model has better prediction accuracy than other machine learning model such as SVM.",

keywords = "Auto encoder, Deep learning, Long short-term memory",

author = "Jinxia Zhang and Xuru Jiang and Xin Chen and Xiaojing Li and Dong Guo and Lixin Cui",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019 ; Conference date: 12-04-2019 Through 15-04-2019",

year = "2019",

month = apr,

day = "12",

doi = "10.1145/3325730.3325735",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "85--89",

booktitle = "ICMAI 2019 - Proceedings of 2019 4th International Conference on Mathematics and Artificial Intelligence",

}

Zhang, J, Jiang, X, Chen, X, Li, X, Guo, D & Cui, L 2019, Wind power generation prediction based on LSTM. in ICMAI 2019 - Proceedings of 2019 4th International Conference on Mathematics and Artificial Intelligence. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 85-89, 4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019, Chegndu, China, 12/04/19. https://doi.org/10.1145/3325730.3325735

Wind power generation prediction based on LSTM. / Zhang, Jinxia; Jiang, Xuru; Chen, Xin et al.
ICMAI 2019 - Proceedings of 2019 4th International Conference on Mathematics and Artificial Intelligence. Association for Computing Machinery, 2019. p. 85-89 (ACM International Conference Proceeding Series).

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

TY - GEN

T1 - Wind power generation prediction based on LSTM

AU - Zhang, Jinxia

AU - Jiang, Xuru

AU - Chen, Xin

AU - Li, Xiaojing

AU - Guo, Dong

AU - Cui, Lixin

PY - 2019/4/12

Y1 - 2019/4/12

N2 - In recent years, with the increasing proportion of wind power generation, the impact of wind power generation on grid security is also growing. This makes the prediction accuracy of wind power generation higher and higher. This paper utilizes the LSTM model of the deep learning domain to predict wind power generation. Besides, Auto Encoder is employed to reduce the data dimension, improve the generalization ability of the model, and shorten the training time. Simulation experiments show that the LSTM model has better prediction accuracy than other machine learning model such as SVM.

AB - In recent years, with the increasing proportion of wind power generation, the impact of wind power generation on grid security is also growing. This makes the prediction accuracy of wind power generation higher and higher. This paper utilizes the LSTM model of the deep learning domain to predict wind power generation. Besides, Auto Encoder is employed to reduce the data dimension, improve the generalization ability of the model, and shorten the training time. Simulation experiments show that the LSTM model has better prediction accuracy than other machine learning model such as SVM.

KW - Auto encoder

KW - Deep learning

KW - Long short-term memory

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U2 - 10.1145/3325730.3325735

DO - 10.1145/3325730.3325735

M3 - Conference contribution

AN - SCOPUS:85068827637

T3 - ACM International Conference Proceeding Series

SP - 85

EP - 89

BT - ICMAI 2019 - Proceedings of 2019 4th International Conference on Mathematics and Artificial Intelligence

PB - Association for Computing Machinery

T2 - 4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019

Y2 - 12 April 2019 through 15 April 2019

ER -

Wind power generation prediction based on LSTM

Abstract

Publication series

Conference

Keywords

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