Ultra-Short-Term Prediction of Wind Power Based on VMD-PE-MulitiBiLSTM

Yeye CHEN; Yao LI; Handong LI

doi:10.16513/j.2096-2185.DE.2409201

TSINGHUA JOURNALS HOME

Source Journal for Chinese Scientific and Technical Papers and Citations

RCCSE Chinese Core Academic Journals

Classification Catalogue of High Quality Scientific Journals in the field of Energy and Power

Classification Catalogue of High Quality Scientific Journals in Coal Field

PDF(6510 KB)

Distributed Energy ›› 2024, Vol. 9 ›› Issue (2) : 1-7. DOI: 10.16513/j.2096-2185.DE.2409201

Basic Research

Ultra-Short-Term Prediction of Wind Power Based on VMD-PE-MulitiBiLSTM

Yeye CHEN ¹ ,
Yao LI ² ,
Handong LI ¹

Author information +

History +

Abstract

In order to reduce the error of ultra-short-term wind power prediction, an ultra-short-term prediction model of wind power based on variational mode decomposition (VMD), permutation entropy (PE) and multilayer bidirectional long short-term memory (MultiBiLSTM) is proposed. Firstly, the historical wind power sequence is decomposed into several sub-modal components using VMD decomposition algorithm, and the sub-modal wind power components are reconstructed according to the calculated PE value. Then, the feature attention (FA) mechanism and deep residual cascade network (DRCnet) are used to construct a MulitiBiLSTM prediction model to predict the reconstructed subsequences. Finally, the predicted value of the sub-sequence is reconstructed to obtain the final prediction result of wind power. The datum set of a wind field in Guizhou province is used to verify the proposed method and compare it with other prediction models. The results show that using VMD-PE-MultiBiLSTM model can significantly reduce the prediction error of wind power.

Key words

ultra-short-term prediction of wind power / variational mode decomposition (VMD) / permutation entropy (PE) / multilayer bidirectional long short-term memory (MultiBiLSTM)

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Yeye CHEN , Yao LI , Handong LI. Ultra-Short-Term Prediction of Wind Power Based on VMD-PE-MulitiBiLSTM[J]. Distributed Energy Resources. 2024, 9(2): 1-7 https://doi.org/10.16513/j.2096-2185.DE.2409201

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[1]	ZHANG F, LI N, LI L, et al. A local semi-supervised ensemble learning strategy for the data-driven soft sensor of the power prediction in wind power generation[J]. Fuel, 2023, 333: 126435. Cited in this article [1]

[2]	WANG Y, HU Q, LI L, et al. Approaches to wind power curve modeling: A review and discussion[J]. Renewable and Sustainable Energy Reviews, 2019, 116: 109422. Cited in this article [1]

[3]	陶然，张涛，冉华军，等. 计及可用输电能力的风电并网系统中STATCOM的双层优化配置[J]. 智慧电力，2022, 50(12): 86-93. TAO Ran, ZHANG Tao, RAN Huajun, et al. Bi-level optimal configuration of STATCOM in wind power grid-connected system considering available transfer capacity[J]. Smart Power, 2022, 50(12): 86-93. Cited in this article [1]

[4]	徐彬，薛帅，高厚磊，等. 海上风电场及其关键技术发展现状与趋势[J]. 发电技术，2022, 43(2): 227-235. XU Bin, XUE Shuai, GAO Houlei, et al. Development status and prospects of offshore wind farms and it's key technology[J]. Power Generation Technology, 2022, 43(2): 227-235. Cited in this article [1]

[5]	黎静华，桑川川，甘一夫，等. 风电功率预测技术研究综述[J]. 现代电力，2017, 34(3): 1-11. LI Jinghua, SANG Chuanchuan, GAN Yifu, et al. A review of researches on wind power forecasting technology[J]. Modern Electric Power, 2017, 34(3): 1-11. Cited in this article [1]

[6]	DALTON A, BEKKER B. Exogenous atmospheric variables as wind speed predictors in machine learning[J]. Applied Energy, 2022, 319: 119257. Cited in this article [1]

[7]	CATALÃO J P S, POUSINHO H M I, MENDES V M F. Short-term wind power forecasting in Portugal by neural networks and wavelet transform[J]. Renewable Energy, 2011, 36(4): 1245-1251. Cited in this article [1]

[8]	LIU Y, LI D, WAN S, et al. A long short-term memory-based model for greenhouse climate prediction[J]. International Journal of Intelligent Systems, 2022, 37(1): 135-151. Cited in this article [2]

[9]	PENG T, ZHANG C, ZHOU J, et al. An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting[J]. Energy, 2021, 221: 119887. Cited in this article [1]

[10]	JOURABLOO A, LIU X. Pose-invariant face alignment via CNN-based dense 3D model fitting[J]. International Journal of Computer Vision, 2017, 124(4): 187-203. Cited in this article [1]

[11]	WU Q, GUAN F, LV C, et al. Ultra-short-term multi-step wind power forecasting based on CNN-LSTM[J]. IET Renewable Power Generation, 2021, 15(5): 1019-1029. Cited in this article [1]

[12]	GRAVES A, SCHMIDHUBER J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks, 2005, 18(5-6): 602-610. Cited in this article [1]

[13]	KO M S, LEE K, KIM J K, et al. Deep concatenated residual network with bidirectional LSTM for one-hour-ahead wind power forecasting[J]. IEEE Transactions on Sustainable Energy, 2021, 12(2): 1321-1335. Cited in this article [3]

[14]	谢丽蓉，王斌，包洪印，等. 基于EEMD-WOA-LSSVM的超短期风电功率预测[J]. 太阳能学报，2021, 42(7): 290-296. XIE Lirong, WANG Bin, BAO Hongyin, et al. Ultra-short term wind power forecast based on EEMD-WOA-LSSVM[J]. Acta Energiae Solaris Sinica, 2021, 42(7): 290-296. Cited in this article [1]

[15]	YU M, NIU D, GAO T, et al. A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism[J]. Energy, 2023, 269: 126738. Cited in this article [1]

[16]	HAN Y, MI L, SHEN L, et al. A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting[J]. Applied Energy, 2022, 312: 118777. Cited in this article [1]

[17]	张亚刚，赵云鹏，王思祺. 基于EVMD和布谷鸟算法的短期风功率区间预测[J]. 太阳能学报，2022, 43(8): 292-299. ZHANG Yagang, ZHAO Yunpeng, WANG Siqi. Short-term wind power interval prediction based on EVMD and Cuckoo algorithm[J]. Acta Energiae Solaris Sinica, 2022, 43(8): 292-299. Cited in this article [1]

[18]

沙骏，徐雨森，刘冲冲，等. 基于变分模态分解和分位数卷积-循环神经网络的短期风功率预测[J]. 中国电力，2022, 55(12): 61-68.

SHA

Jun

, XU

Yusen

, LIU

Chongchong

, et al. Short-term wind power prediction based on variational mode decomposition and quantile convolutional recurrent neural networks[J]. Electric Power, 2022, 55(12): 61-68.

Cited in this article [1]

[19]

武小梅，林翔，谢旭泉，等. 基于VMD-PE和优化相关向量机的短期风电功率预测[J]. 太阳能学报，2018, 39(11): 3277-3285.

Xiaomei

, LIN

Xiang

, XIE

Xuquan

, et al. Short-term wind power prediction based on variational mode factory-permutation entropy and optimized correlation vector machine[J]. Acta Energiae Solaris Sinica, 2018, 39(11): 3277-3285.

Cited in this article [1]

[20]	DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 62(3): 531-544. Cited in this article [1]

[21]	BANDT C, POMPE B. Permutation entropy: A natural complexity measure of time series[J]. Physical Review Letters, 2002, 88(17): 174102. Cited in this article [2]

[22]	XU Z, LI C, YANG Y. Fault diagnosis of rolling bearings using an improved multi-scale convolutional neural network with feature attention mechanism[J]. ISA Transactions, 2021, 110: 379-393. Cited in this article [1]