An improved BP neural network method for Wind Power Prediction

Zhonghao Li; Ping Ma; Xin Wang; Jieyu Xu; Xiaodong Wan

doi:10.1109/CIEEC54735.2022.9846833

An improved BP neural network method for Wind Power Prediction

Zhonghao Li^*
, Ping Ma
, Xin Wang
, Jieyu Xu
, Xiaodong Wan

^*Corresponding author for this work

Qingdao University

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

6 Scopus citations

Abstract

For large wind farms with grid-connected power generation systems, power prediction is very important. A sparrow search algorithm is proposed for optimizing the wind power prediction in BP neural network to improve the accuracy and stability of wind power prediction. Tent chaotic mapping gets used to increase the species diversity of the sparrow search algorithm and improve the ability of the algorithm to step out of local optimization and global search. The improved SSA-BP algorithm and traditional BP neural network method are used to predict the wind power. The prediction results and errors of the two methods are compared. The comparative analysis of the actual measured data and model prediction data shows that the SSA-BP method can better track the trend of the actual wind speed data, reduce the error and improve the prediction accuracy.

Original language	English
Title of host publication	Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	653-658
Number of pages	6
ISBN (Electronic)	9781665411042
DOIs	https://doi.org/10.1109/CIEEC54735.2022.9846833
State	Published - 2022
Externally published	Yes
Event	5th IEEE International Electrical and Energy Conference, CIEEC 2022 - Nanjing, China Duration: 27 May 2022 → 29 May 2022

Publication series

Name	Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022

Conference

Conference	5th IEEE International Electrical and Energy Conference, CIEEC 2022
Country/Territory	China
City	Nanjing
Period	27/05/22 → 29/05/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

BP neural network
Power prediction
Sparrow search algorithm
Wind power generation

Access to Document

10.1109/CIEEC54735.2022.9846833

Cite this

Li, Z., Ma, P., Wang, X., Xu, J., & Wan, X. (2022). An improved BP neural network method for Wind Power Prediction. In Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022 (pp. 653-658). (Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIEEC54735.2022.9846833

@inproceedings{8b1f68f8717a47c897e81e9584a43dac,

title = "An improved BP neural network method for Wind Power Prediction",

abstract = "For large wind farms with grid-connected power generation systems, power prediction is very important. A sparrow search algorithm is proposed for optimizing the wind power prediction in BP neural network to improve the accuracy and stability of wind power prediction. Tent chaotic mapping gets used to increase the species diversity of the sparrow search algorithm and improve the ability of the algorithm to step out of local optimization and global search. The improved SSA-BP algorithm and traditional BP neural network method are used to predict the wind power. The prediction results and errors of the two methods are compared. The comparative analysis of the actual measured data and model prediction data shows that the SSA-BP method can better track the trend of the actual wind speed data, reduce the error and improve the prediction accuracy.",

keywords = "BP neural network, Power prediction, Sparrow search algorithm, Wind power generation",

author = "Zhonghao Li and Ping Ma and Xin Wang and Jieyu Xu and Xiaodong Wan",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 5th IEEE International Electrical and Energy Conference, CIEEC 2022 ; Conference date: 27-05-2022 Through 29-05-2022",

year = "2022",

doi = "10.1109/CIEEC54735.2022.9846833",

language = "英语",

series = "Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022",

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

pages = "653--658",

booktitle = "Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022",

address = "美国",

}

Li, Z, Ma, P, Wang, X, Xu, J & Wan, X 2022, An improved BP neural network method for Wind Power Prediction. in Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022. Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022, Institute of Electrical and Electronics Engineers Inc., pp. 653-658, 5th IEEE International Electrical and Energy Conference, CIEEC 2022, Nanjing, China, 27/05/22. https://doi.org/10.1109/CIEEC54735.2022.9846833

An improved BP neural network method for Wind Power Prediction. / Li, Zhonghao; Ma, Ping; Wang, Xin et al.
Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 653-658 (Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022).

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

TY - GEN

T1 - An improved BP neural network method for Wind Power Prediction

AU - Li, Zhonghao

AU - Ma, Ping

AU - Wang, Xin

AU - Xu, Jieyu

AU - Wan, Xiaodong

PY - 2022

Y1 - 2022

N2 - For large wind farms with grid-connected power generation systems, power prediction is very important. A sparrow search algorithm is proposed for optimizing the wind power prediction in BP neural network to improve the accuracy and stability of wind power prediction. Tent chaotic mapping gets used to increase the species diversity of the sparrow search algorithm and improve the ability of the algorithm to step out of local optimization and global search. The improved SSA-BP algorithm and traditional BP neural network method are used to predict the wind power. The prediction results and errors of the two methods are compared. The comparative analysis of the actual measured data and model prediction data shows that the SSA-BP method can better track the trend of the actual wind speed data, reduce the error and improve the prediction accuracy.

AB - For large wind farms with grid-connected power generation systems, power prediction is very important. A sparrow search algorithm is proposed for optimizing the wind power prediction in BP neural network to improve the accuracy and stability of wind power prediction. Tent chaotic mapping gets used to increase the species diversity of the sparrow search algorithm and improve the ability of the algorithm to step out of local optimization and global search. The improved SSA-BP algorithm and traditional BP neural network method are used to predict the wind power. The prediction results and errors of the two methods are compared. The comparative analysis of the actual measured data and model prediction data shows that the SSA-BP method can better track the trend of the actual wind speed data, reduce the error and improve the prediction accuracy.

KW - BP neural network

KW - Power prediction

KW - Sparrow search algorithm

KW - Wind power generation

UR - https://www.scopus.com/pages/publications/85137379137

U2 - 10.1109/CIEEC54735.2022.9846833

DO - 10.1109/CIEEC54735.2022.9846833

M3 - 会议稿件

AN - SCOPUS:85137379137

T3 - Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022

SP - 653

EP - 658

BT - Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Electrical and Energy Conference, CIEEC 2022

Y2 - 27 May 2022 through 29 May 2022

ER -

Li Z, Ma P, Wang X, Xu J, Wan X. An improved BP neural network method for Wind Power Prediction. In Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 653-658. (Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022). doi: 10.1109/CIEEC54735.2022.9846833

An improved BP neural network method for Wind Power Prediction

Abstract

Publication series

Conference

UN SDGs

Keywords

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