GPR-Based Wind Power Probabilistic Prediction Model Considering Multiple Meteorological Factors

Song Cheng; Jing Ren; Xin Zhou; Min Gao; Meilun Guo; Peng Kou

doi:10.1109/ICPES56491.2022.10073194

GPR-Based Wind Power Probabilistic Prediction Model Considering Multiple Meteorological Factors

Song Cheng
, Jing Ren
, Xin Zhou
, Min Gao
, Meilun Guo
, Peng Kou

School of Electrical Engineering

State Grid Corporation of China
Xi'an Jiaotong University

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

5 Scopus citations

Abstract

Nowadays, the accurate prediction of wind power has been a topical and challenging issue. Due to the random and intermittent nature of wind power, traditional models are not sufficient to achieve accurate prediction. Therefore, this paper proposed a wind power probabilistic prediction model considering multiple meteorological factors based on Gaussian process regression (GPR). First, suitable meteorological factors are selected based on correlation analysis between historical meteorological factors and wind power data. Then, GPR model with suitable meteorological factors and historical wind power data as input is used to make probabilistic prediction. The simulation results and error analysis show that the model proposed in this paper is feasible and can effectively improve wind power prediction accuracy.

Original language	English
Title of host publication	2022 12th International Conference on Power and Energy Systems, ICPES 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	790-794
Number of pages	5
ISBN (Electronic)	9781665451451
DOIs	https://doi.org/10.1109/ICPES56491.2022.10073194
State	Published - 2022
Event	12th International Conference on Power and Energy Systems, ICPES 2022 - Guangzhou, China Duration: 23 Dec 2022 → 25 Dec 2022

Publication series

Name	2022 12th International Conference on Power and Energy Systems, ICPES 2022

Conference

Conference	12th International Conference on Power and Energy Systems, ICPES 2022
Country/Territory	China
City	Guangzhou
Period	23/12/22 → 25/12/22

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Gaussian process regression
Wind power prediction
correlation analysis
probabilistic prediction

Access to Document

10.1109/ICPES56491.2022.10073194

Cite this

Cheng, S., Ren, J., Zhou, X., Gao, M., Guo, M., & Kou, P. (2022). GPR-Based Wind Power Probabilistic Prediction Model Considering Multiple Meteorological Factors. In 2022 12th International Conference on Power and Energy Systems, ICPES 2022 (pp. 790-794). (2022 12th International Conference on Power and Energy Systems, ICPES 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPES56491.2022.10073194

@inproceedings{5dd7f82599b14501801fd03dcef55a6b,

title = "GPR-Based Wind Power Probabilistic Prediction Model Considering Multiple Meteorological Factors",

abstract = "Nowadays, the accurate prediction of wind power has been a topical and challenging issue. Due to the random and intermittent nature of wind power, traditional models are not sufficient to achieve accurate prediction. Therefore, this paper proposed a wind power probabilistic prediction model considering multiple meteorological factors based on Gaussian process regression (GPR). First, suitable meteorological factors are selected based on correlation analysis between historical meteorological factors and wind power data. Then, GPR model with suitable meteorological factors and historical wind power data as input is used to make probabilistic prediction. The simulation results and error analysis show that the model proposed in this paper is feasible and can effectively improve wind power prediction accuracy.",

keywords = "Gaussian process regression, Wind power prediction, correlation analysis, probabilistic prediction",

author = "Song Cheng and Jing Ren and Xin Zhou and Min Gao and Meilun Guo and Peng Kou",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 12th International Conference on Power and Energy Systems, ICPES 2022 ; Conference date: 23-12-2022 Through 25-12-2022",

year = "2022",

doi = "10.1109/ICPES56491.2022.10073194",

language = "英语",

series = "2022 12th International Conference on Power and Energy Systems, ICPES 2022",

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

pages = "790--794",

booktitle = "2022 12th International Conference on Power and Energy Systems, ICPES 2022",

}

Cheng, S, Ren, J, Zhou, X, Gao, M, Guo, M & Kou, P 2022, GPR-Based Wind Power Probabilistic Prediction Model Considering Multiple Meteorological Factors. in 2022 12th International Conference on Power and Energy Systems, ICPES 2022. 2022 12th International Conference on Power and Energy Systems, ICPES 2022, Institute of Electrical and Electronics Engineers Inc., pp. 790-794, 12th International Conference on Power and Energy Systems, ICPES 2022, Guangzhou, China, 23/12/22. https://doi.org/10.1109/ICPES56491.2022.10073194

GPR-Based Wind Power Probabilistic Prediction Model Considering Multiple Meteorological Factors. / Cheng, Song; Ren, Jing; Zhou, Xin et al.
2022 12th International Conference on Power and Energy Systems, ICPES 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 790-794 (2022 12th International Conference on Power and Energy Systems, ICPES 2022).

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

TY - GEN

T1 - GPR-Based Wind Power Probabilistic Prediction Model Considering Multiple Meteorological Factors

AU - Cheng, Song

AU - Ren, Jing

AU - Zhou, Xin

AU - Gao, Min

AU - Guo, Meilun

AU - Kou, Peng

PY - 2022

Y1 - 2022

N2 - Nowadays, the accurate prediction of wind power has been a topical and challenging issue. Due to the random and intermittent nature of wind power, traditional models are not sufficient to achieve accurate prediction. Therefore, this paper proposed a wind power probabilistic prediction model considering multiple meteorological factors based on Gaussian process regression (GPR). First, suitable meteorological factors are selected based on correlation analysis between historical meteorological factors and wind power data. Then, GPR model with suitable meteorological factors and historical wind power data as input is used to make probabilistic prediction. The simulation results and error analysis show that the model proposed in this paper is feasible and can effectively improve wind power prediction accuracy.

AB - Nowadays, the accurate prediction of wind power has been a topical and challenging issue. Due to the random and intermittent nature of wind power, traditional models are not sufficient to achieve accurate prediction. Therefore, this paper proposed a wind power probabilistic prediction model considering multiple meteorological factors based on Gaussian process regression (GPR). First, suitable meteorological factors are selected based on correlation analysis between historical meteorological factors and wind power data. Then, GPR model with suitable meteorological factors and historical wind power data as input is used to make probabilistic prediction. The simulation results and error analysis show that the model proposed in this paper is feasible and can effectively improve wind power prediction accuracy.

KW - Gaussian process regression

KW - Wind power prediction

KW - correlation analysis

KW - probabilistic prediction

UR - https://www.scopus.com/pages/publications/85152193015

U2 - 10.1109/ICPES56491.2022.10073194

DO - 10.1109/ICPES56491.2022.10073194

M3 - 会议稿件

AN - SCOPUS:85152193015

T3 - 2022 12th International Conference on Power and Energy Systems, ICPES 2022

SP - 790

EP - 794

BT - 2022 12th International Conference on Power and Energy Systems, ICPES 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th International Conference on Power and Energy Systems, ICPES 2022

Y2 - 23 December 2022 through 25 December 2022

ER -

Cheng S, Ren J, Zhou X, Gao M, Guo M, Kou P. GPR-Based Wind Power Probabilistic Prediction Model Considering Multiple Meteorological Factors. In 2022 12th International Conference on Power and Energy Systems, ICPES 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 790-794. (2022 12th International Conference on Power and Energy Systems, ICPES 2022). doi: 10.1109/ICPES56491.2022.10073194

GPR-Based Wind Power Probabilistic Prediction Model Considering Multiple Meteorological Factors

Abstract

Publication series

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UN SDGs

Keywords

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