A study of grey theory used in prediction of annual wind power generation

Chengwei Tian; Lei Dong; Shuang Gao; Xiaozhong Liao

doi:10.1109/ICEICE.2011.5777141

A study of grey theory used in prediction of annual wind power generation

Chengwei Tian^*
, Lei Dong
, Shuang Gao
, Xiaozhong Liao

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

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

4 Citations (Scopus)

Abstract

With the coming mature of the wind energy technology, wind energy has become one of the most promising renewable energy. In order to conduct post appraisals and operation management to a large wind farm, accurate prediction of the annual wind power generation is necessary. In this paper, grey model GM (1,1) for predicting annual wind power generation is set up. Moreover, in order to improve the prediction accuracy, a effective method of processing the original wind power data series is proposed. The prediction result with the original data series processed is compared to the unprocessed one. We obtain that the normalized average absolute error of the prediction result with the original data series processed is 7.0315%, improved 0.7679% relative to that original data series unprocessed.

Original language	English
Title of host publication	2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings
Pages	1952-1955
Number of pages	4
DOIs	https://doi.org/10.1109/ICEICE.2011.5777141
Publication status	Published - 2011
Event	2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Wuhan, China Duration: 15 Apr 2011 → 17 Apr 2011

Publication series

Name	2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings

Conference

Conference	2011 International Conference on Electric Information and Control Engineering, ICEICE 2011
Country/Territory	China
City	Wuhan
Period	15/04/11 → 17/04/11

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

grey predicting model
information renewal model
wind power generation prediction

Access to Document

10.1109/ICEICE.2011.5777141

Cite this

Tian, C., Dong, L., Gao, S., & Liao, X. (2011). A study of grey theory used in prediction of annual wind power generation. In 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings (pp. 1952-1955). Article 5777141 (2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings). https://doi.org/10.1109/ICEICE.2011.5777141

@inproceedings{f8c686aec0724eb29b2d6d683285203c,

title = "A study of grey theory used in prediction of annual wind power generation",

abstract = "With the coming mature of the wind energy technology, wind energy has become one of the most promising renewable energy. In order to conduct post appraisals and operation management to a large wind farm, accurate prediction of the annual wind power generation is necessary. In this paper, grey model GM (1,1) for predicting annual wind power generation is set up. Moreover, in order to improve the prediction accuracy, a effective method of processing the original wind power data series is proposed. The prediction result with the original data series processed is compared to the unprocessed one. We obtain that the normalized average absolute error of the prediction result with the original data series processed is 7.0315\%, improved 0.7679\% relative to that original data series unprocessed.",

keywords = "grey predicting model, information renewal model, wind power generation prediction",

author = "Chengwei Tian and Lei Dong and Shuang Gao and Xiaozhong Liao",

year = "2011",

doi = "10.1109/ICEICE.2011.5777141",

language = "English",

isbn = "9781424480395",

series = "2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings",

pages = "1952--1955",

booktitle = "2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings",

note = "2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 ; Conference date: 15-04-2011 Through 17-04-2011",

}

Tian, C, Dong, L, Gao, S & Liao, X 2011, A study of grey theory used in prediction of annual wind power generation. in 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings., 5777141, 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings, pp. 1952-1955, 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011, Wuhan, China, 15/04/11. https://doi.org/10.1109/ICEICE.2011.5777141

A study of grey theory used in prediction of annual wind power generation. / Tian, Chengwei; Dong, Lei; Gao, Shuang et al.
2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings. 2011. p. 1952-1955 5777141 (2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings).

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

TY - GEN

T1 - A study of grey theory used in prediction of annual wind power generation

AU - Tian, Chengwei

AU - Dong, Lei

AU - Gao, Shuang

AU - Liao, Xiaozhong

PY - 2011

Y1 - 2011

N2 - With the coming mature of the wind energy technology, wind energy has become one of the most promising renewable energy. In order to conduct post appraisals and operation management to a large wind farm, accurate prediction of the annual wind power generation is necessary. In this paper, grey model GM (1,1) for predicting annual wind power generation is set up. Moreover, in order to improve the prediction accuracy, a effective method of processing the original wind power data series is proposed. The prediction result with the original data series processed is compared to the unprocessed one. We obtain that the normalized average absolute error of the prediction result with the original data series processed is 7.0315%, improved 0.7679% relative to that original data series unprocessed.

AB - With the coming mature of the wind energy technology, wind energy has become one of the most promising renewable energy. In order to conduct post appraisals and operation management to a large wind farm, accurate prediction of the annual wind power generation is necessary. In this paper, grey model GM (1,1) for predicting annual wind power generation is set up. Moreover, in order to improve the prediction accuracy, a effective method of processing the original wind power data series is proposed. The prediction result with the original data series processed is compared to the unprocessed one. We obtain that the normalized average absolute error of the prediction result with the original data series processed is 7.0315%, improved 0.7679% relative to that original data series unprocessed.

KW - grey predicting model

KW - information renewal model

KW - wind power generation prediction

UR - https://www.scopus.com/pages/publications/79959915369

U2 - 10.1109/ICEICE.2011.5777141

DO - 10.1109/ICEICE.2011.5777141

M3 - Conference contribution

AN - SCOPUS:79959915369

SN - 9781424480395

T3 - 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings

SP - 1952

EP - 1955

BT - 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings

T2 - 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011

Y2 - 15 April 2011 through 17 April 2011

ER -

Tian C, Dong L, Gao S, Liao X. A study of grey theory used in prediction of annual wind power generation. In 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings. 2011. p. 1952-1955. 5777141. (2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings). doi: 10.1109/ICEICE.2011.5777141

A study of grey theory used in prediction of annual wind power generation

Abstract

Publication series

Conference

UN SDGs

Keywords

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