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Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Research Article

Decentralized Wind Power Forecasting Method Based on Informer

Author(s): Yuting Yan, Wei Li*, Shi Su, Hao Bai, Yang Yang, Shuhui Pan, Zhiyong Yuan, Jun Chen and Qindao Zhao

Volume 15, Issue 8, 2022

Published on: 26 September, 2022

Page: [679 - 687] Pages: 9

DOI: 10.2174/2352096515666220818122603

Price: $65

Abstract

Background: Improving the accuracy of decentralized wind power forecasting has become an important means to ensure the safe and stable operation of the power grid. Traditional prediction methods cannot meet the requirements of precise wind power prediction for long time series.

Methods: A decentralized wind power prediction model based on Informer is established. Introducing the model's sparse self-attention mechanism, self-attention distillation mechanism and The generative decoder model realizes the long-term sequence power prediction of decentralized wind power.

Results: The results of the proposed model are analyzed by comparing it with traditional neural network models such as LSTM and feed-forward neural networks. The root mean square error (RMSE) can be reduced by up to 6.3%.

Conclusion: It is proved that the Informer model prediction has a lower prediction error and performs better in long-term sequence power pre-tasks.

Keywords: Decentralized wind, power, wind power, time series, forecast, informer

Graphical Abstract

[1]
Y. Xue, L. Xing, X. Feng, Y. Chen, C. Dong, F. Wen, and J. Ping, "Comments on the impact of wind power uncertainty on power systems", Zhongguo Dianji Gongcheng Xuebao, vol. 34, no. 29, pp. 5029-5040, 2014.
[2]
G. Xie, Y. He, and Q. Wang, "Analysis on the development prospects of wind power in the eastern and central regions of China", China Electric Power, vol. 51, no. 03, pp. 100-104, 2018.
[3]
G. Xinneng, "Notice of the national energy administration on printing and distributing guiding opinions on the development and construc-tion of decentralized access wind power projects", Norwegian Energy and Environment Consortium, 2021.
[4]
Q. Zhu, H. Li, Z. Wang, J. Chen, and W. Bo, "Ultra short term prediction of wind farm power generation based on long short term memory network", Power Sys. Technol., vol. 41, no. 12, pp. 3797-3802, 2017.
[5]
X. Yang, W. Lin, W. Shu, Y. Zhang, and Z. Ning, "An ultra-short-term wind power prediction model combining CNN and GRU networks", Renew. Energy, vol. 37, no. 03, pp. 456-462, 2019.
[6]
N.I.U. Zhewen, Y.U. Zeyuan, and L.I. Bo, "Short-term wind power forecasting model based on deep gated recurrent unit neural network", Elect. Power. Auto. Equip., vol. 38, no. 5, pp. 36-42, 2018.
[7]
X. Yang, W. Guan, Y. Liu, and Y. Xiao, "Wind power interval prediction method based on particle swarm optimization kernel extreme learning machine model", Zhongguo Dianji Gongcheng Xuebao, vol. 35, no. S1, pp. 146-153, 2015.
[8]
M.J. Sanjari, H.B. Gooi, and N.C. Nair, "Power generation forecast of hybrid PV–wind system", IEEE Trans. Sustain. Energy, vol. 11, no. 2, pp. 703-712, 2020.
[http://dx.doi.org/10.1109/TSTE.2019.2903900]
[9]
L.I.U. Kewen, P.U. Tianjiao, and Z.H.O.U. Haiming, "In A short term wind power forecasting model based on combination algorithms", Proceedings of the CSEE, vol. 33, no. 34, pp. 130-135, 2013.
[10]
L. Ye, and P. Liu, "Combined model based on EMD-SVM for short-term wind power prediction", Zhongguo Dianji Gongcheng Xuebao, vol. 31, no. 31, pp. 102-108, 2011.
[11]
D. Yanan, P. Zhengning, and Q. Jingxian, "Ultra-short-term wind power output prediction based on ls-wmc combined model", J. Phys. Conf. Ser., vol. 10, no. 5, 2021.
[12]
Li Zhuo, Ye Lin, Binhua Dai, Yijun Yu, Yadi Luo, and Xuri Song, "Ultra-short-term wind power prediction method based on IDSCNN-AM-LSTM combined neural network", High volt. technol., pp. 1-13, 2022.
[13]
A. Khan, H. Chen, K. Mehmood, M.F. Tahir, M.S. Javed, and N.A. Larik, "Short term load forecasting using bootstrap aggregating based ensemble artificial neural network", Recent Adv. Electr. Electron. Eng., vol. 13, no. 7, 2020.
[14]
A. Vaswani, N. Shazeer, and N. Parmar, "Attention is all you need", Adv. Neural Inf. Process. Syst., pp. 5998-6008, 2017.
[15]
H. Zhou, S. Zhang, and J. Peng, "In: Beyond efficient Transformer for long sequence time-series forecasting", Proceedings of AAAI, vol. 35, no. 12, 2021.
[16]
X. Wang, Y. Zhang, Y. Liu, and Y. Miao, "Energy storage peak shaving control strategy based on dual time scale net load forecasting", Acta. Solar Energy, vol. 42, no. 07, pp. 58-64, 2021.
[17]
X. Man, Q. Ying, and Z. Lu, "Comprehensive evaluation method for short-term wind power forecast errors", Dianli Xitong Zidonghua, vol. 35, no. 12, pp. 20-26, 2011.
[18]
D. Ming, Z. Chao, W. Bo, B. Rui, L. Miao, and J. Che, "Wind power short-term prediction and error correction based on power fluctuation process", Dianli Xitong Zidonghua, vol. 43, no. 03, pp. 2-9, 2019.
[19]
Y. Cao, S. Yan, H. Liu, and G. Li, "Short-term wind power prediction method based on noise reduction time series deep learning network", J. Elect. Power Sys. Auto., vol. 32, no. 01, pp. 145-150, 2020.

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