An ultra-short-term wind power prediction method based on CNN-LSTM

Wenbo Zhou; Ming Xin; Yanli Wang; Chen Yang; Songsong Liu; Ruizhi Zhang; Xudong Liu; Lina Zhou

doi:10.1109/IAEAC59436.2024.10503703

An ultra-short-term wind power prediction method based on CNN-LSTM

Wenbo Zhou
, Ming Xin
, Yanli Wang
, Chen Yang
, Songsong Liu^*
, Ruizhi Zhang
, Xudong Liu
, Lina Zhou

^*Corresponding author for this work

Research Institute
School of Management, Harbin Institute of Technology
Heilongjiang Institute of Technology

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

9 Scopus citations

Abstract

In order to improve the precision of wind power prediction, a convolutional neural networks-long short-term memory combination method for ultra-short term wind power prediction is proposed. First, a CNN-LSTM ultra-short-term wind power prediction model is built. In the CNN-LSTM model, CNN is used for feature processing of wind power data sets, and it is used as the data input of LSTM model, so as to establish a CNN-LSTM fusion prediction model. The effectiveness of the combined model is verified by analyzing the Numerical Weather Prediction data and historical observation data of a wind farm. The proposed model is compared with various comparative models, leading to the important conclusion the combination model has higher prediction accuracy.

Original language	English
Title of host publication	IAEAC 2024 - IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference
Editors	Bing Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1007-1011
Number of pages	5
ISBN (Electronic)	9798350339161
DOIs	https://doi.org/10.1109/IAEAC59436.2024.10503703
State	Published - 2024
Externally published	Yes
Event	7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024 - Chongqing, China Duration: 15 Mar 2024 → 17 Mar 2024

Publication series

Name	IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
ISSN (Print)	2689-6621

Conference

Conference	7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024
Country/Territory	China
City	Chongqing
Period	15/03/24 → 17/03/24

Keywords

Combination model
convolutional neural networks(CNN)
long short-term memory (LSTM)
ultra-short-term wind power prediction

Access to Document

10.1109/IAEAC59436.2024.10503703

Cite this

Zhou, W., Xin, M., Wang, Y., Yang, C., Liu, S., Zhang, R., Liu, X., & Zhou, L. (2024). An ultra-short-term wind power prediction method based on CNN-LSTM. In B. Xu (Ed.), IAEAC 2024 - IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (pp. 1007-1011). (IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAEAC59436.2024.10503703

Zhou, Wenbo ; Xin, Ming ; Wang, Yanli et al. / An ultra-short-term wind power prediction method based on CNN-LSTM. IAEAC 2024 - IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference. editor / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 1007-1011 (IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)).

@inproceedings{1318944916bf47d1be0979b2465439d6,

title = "An ultra-short-term wind power prediction method based on CNN-LSTM",

abstract = "In order to improve the precision of wind power prediction, a convolutional neural networks-long short-term memory combination method for ultra-short term wind power prediction is proposed. First, a CNN-LSTM ultra-short-term wind power prediction model is built. In the CNN-LSTM model, CNN is used for feature processing of wind power data sets, and it is used as the data input of LSTM model, so as to establish a CNN-LSTM fusion prediction model. The effectiveness of the combined model is verified by analyzing the Numerical Weather Prediction data and historical observation data of a wind farm. The proposed model is compared with various comparative models, leading to the important conclusion the combination model has higher prediction accuracy.",

keywords = "Combination model, convolutional neural networks(CNN), long short-term memory (LSTM), ultra-short-term wind power prediction",

author = "Wenbo Zhou and Ming Xin and Yanli Wang and Chen Yang and Songsong Liu and Ruizhi Zhang and Xudong Liu and Lina Zhou",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024 ; Conference date: 15-03-2024 Through 17-03-2024",

year = "2024",

doi = "10.1109/IAEAC59436.2024.10503703",

language = "英语",

series = "IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1007--1011",

editor = "Bing Xu",

booktitle = "IAEAC 2024 - IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference",

address = "美国",

}

Zhou, W, Xin, M, Wang, Y, Yang, C, Liu, S, Zhang, R, Liu, X & Zhou, L 2024, An ultra-short-term wind power prediction method based on CNN-LSTM. in B Xu (ed.), IAEAC 2024 - IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference. IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Institute of Electrical and Electronics Engineers Inc., pp. 1007-1011, 7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024, Chongqing, China, 15/03/24. https://doi.org/10.1109/IAEAC59436.2024.10503703

An ultra-short-term wind power prediction method based on CNN-LSTM. / Zhou, Wenbo; Xin, Ming; Wang, Yanli et al.
IAEAC 2024 - IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2024. p. 1007-1011 (IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)).

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

TY - GEN

T1 - An ultra-short-term wind power prediction method based on CNN-LSTM

AU - Zhou, Wenbo

AU - Xin, Ming

AU - Wang, Yanli

AU - Yang, Chen

AU - Liu, Songsong

AU - Zhang, Ruizhi

AU - Liu, Xudong

AU - Zhou, Lina

PY - 2024

Y1 - 2024

N2 - In order to improve the precision of wind power prediction, a convolutional neural networks-long short-term memory combination method for ultra-short term wind power prediction is proposed. First, a CNN-LSTM ultra-short-term wind power prediction model is built. In the CNN-LSTM model, CNN is used for feature processing of wind power data sets, and it is used as the data input of LSTM model, so as to establish a CNN-LSTM fusion prediction model. The effectiveness of the combined model is verified by analyzing the Numerical Weather Prediction data and historical observation data of a wind farm. The proposed model is compared with various comparative models, leading to the important conclusion the combination model has higher prediction accuracy.

AB - In order to improve the precision of wind power prediction, a convolutional neural networks-long short-term memory combination method for ultra-short term wind power prediction is proposed. First, a CNN-LSTM ultra-short-term wind power prediction model is built. In the CNN-LSTM model, CNN is used for feature processing of wind power data sets, and it is used as the data input of LSTM model, so as to establish a CNN-LSTM fusion prediction model. The effectiveness of the combined model is verified by analyzing the Numerical Weather Prediction data and historical observation data of a wind farm. The proposed model is compared with various comparative models, leading to the important conclusion the combination model has higher prediction accuracy.

KW - Combination model

KW - convolutional neural networks(CNN)

KW - long short-term memory (LSTM)

KW - ultra-short-term wind power prediction

UR - https://www.scopus.com/pages/publications/85191980027

U2 - 10.1109/IAEAC59436.2024.10503703

DO - 10.1109/IAEAC59436.2024.10503703

M3 - 会议稿件

AN - SCOPUS:85191980027

T3 - IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

SP - 1007

EP - 1011

BT - IAEAC 2024 - IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference

A2 - Xu, Bing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

Y2 - 15 March 2024 through 17 March 2024

ER -

Zhou W, Xin M, Wang Y, Yang C, Liu S, Zhang R et al. An ultra-short-term wind power prediction method based on CNN-LSTM. In Xu B, editor, IAEAC 2024 - IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference. Institute of Electrical and Electronics Engineers Inc. 2024. p. 1007-1011. (IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)). doi: 10.1109/IAEAC59436.2024.10503703

An ultra-short-term wind power prediction method based on CNN-LSTM

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