基于CEEMDAN二次分解和LSTM的风速多步预测研究

向玲; 刘佳宁; 苏浩; 胡爱军; 朱泽宁

doi:10.19912/j.0254-0096.tynxb.2020-1410

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (8) : 334-339. DOI: 10.19912/j.0254-0096.tynxb.2020-1410

基于CEEMDAN二次分解和LSTM的风速多步预测研究

向玲, 刘佳宁, 苏浩, 胡爱军, 朱泽宁

作者信息 +

RESEARCH ON MULTI-STEP WIND SPEED FORECAST BASED ON CEEMDAN SECONDARY DECOMPOSITION AND LSTM

Xiang Ling, Liu Jianing, Su Hao, Hu Aijun, Zhu Zening

Author information +

文章历史 +

摘要

为了提高风速预测的准确性,提出一种基于自适应噪声完备经验模态分解（CEEMDAN）二次分解和长短时记忆（LSTM）网络的风速多步预测方法。该方法首先应用变分模态分解（VMD）将原始风速序列进行一次分解,充分利用其分解后的残余分量并采用CEEMDAN方法进行二次分解;然后将分解后的所有子序列分别输入到LSTM模型中进行风速多步预测;最后将各模型输出结果进行叠加获得预测风速。以内蒙古某风电场实测数据为例进行建模和预测分析,结果表明所提出的风速多步预测模型具有较高的预测精度,具备实际应用的可行性。

Abstract

In order to improve the accuracy of wind speed prediction, a multi-step wind speed forecast method based on CEEMDAN secondary decomposition and long short-term memory （LSTM） network is proposed. Firstly, variational mode decomposition （VMD） is utilized to decompose the original wind speed series. The obtained residual components are subjected to secondary decomposition by using adaptive noise complete empirical mode decomposition （CEEMDAN） method. Then all the decomposed subsequences are input to LSTM models for training and predicting. Finally, the outputs of all subsequence models are superimposed to obtain the predicted wind speed. Taking the measured data of a wind farm in Inner Mongolia as an example, the results indicate that the proposed multi-step wind speed prediction model has higher prediction accuracy and has the feasibility of practical application.

导出引用

向玲, 刘佳宁, 苏浩, 胡爱军, 朱泽宁. 基于CEEMDAN二次分解和LSTM的风速多步预测研究[J]. 太阳能学报. 2022, 43(8): 334-339 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1410

Xiang Ling, Liu Jianing, Su Hao, Hu Aijun, Zhu Zening. RESEARCH ON MULTI-STEP WIND SPEED FORECAST BASED ON CEEMDAN SECONDARY DECOMPOSITION AND LSTM[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 334-339 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1410

中图分类号： TM614

参考文献

[1] WANG J J, ZHANG W Y, LI Y N, et al.Forecasting wind speed using empirical mode decomposition and Elman neural network[J]. Applied soft computing journal, 2014, 23: 452-459.
[2] MI X W, LIU H, LI Y F.Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine[J]. Energy conversion and management, 2019, 180: 196-205.
[3] 孙斌, 姚海涛. 基于局域均值分解和多核最小二乘支持向量机的短期风速预测[J]. 太阳能学报, 2013, 34(9): 1567-1573.
SUN B, YAO H T.Short-term wind speed forecasting based on local mean decomposition and multi-kernel support vector machine[J]. Acta energiae solaris sinica, 2013, 34(9): 1567-1573.
[4] 叶瑞丽, 郭志忠, 刘瑞叶, 等. 基于小波包分解和改进Elman神经网络的风电场风速和风电功率预测[J]. 电工技术学报, 2017, 32(21): 103-111.
YE R L, GUO Z Z, LIU R Y, et al.Wind speed and wind power forecasting method based on wavelet packet decomposition and improved Elman neural network[J]. Transactions of China Electrotechnical Society, 2017, 32(21): 103-111.
[5] 高桂革, 原阔, 曾宪文, 等. 基于改进CEEMD-CS-ELM的短期风速预测[J]. 太阳能学报, 2021, 42(7): 284-289.
GAO G G, YUAN K, ZENG X W, et al.Short-term wind speed prediction based on improved CEEMD-CS-ELM[J]. Acta energiae solaris sinica, 2021, 42(7): 284-289.
[6] 向玲, 邓泽奇. 基于改进经验小波变换和最小二乘支持向量机的短期风速预测[J]. 太阳能学报, 2021, 42(2): 97-103.
XIANG L, DENG Z Q.Short-term wind speed forecasting based on improved empirical wavelet transform and least squares support vector machines[J]. Acta energiae solaris sinica, 2021, 42(2): 97-103.
[7] 李春祥, 李洲. 基于多种信号分解的台风风速多步预测[J]. 振动测试与诊断, 2019, 39(5): 1103-1110, 1139.
LI C X, LI Z.Multiple signal decomposition method for multi-step forecasting of typhoon wind speed[J]. Journal of vibration, measurement & diagnosis, 2019, 39(5): 1103-1110, 1139.
[8] 张妍, 韩璞, 王东风, 等. 基于变分模态分解和LSSVM的风电场短期风速预测[J]. 太阳能学报, 2018, 39(1): 194-202.
ZHANG Y, HAN P, WANG D F, et al.Short-term prediction of wind speed for wind farm based on variational mode decomposition and LSSVM model[J]. Acta energiae solaris sinica, 2018, 39(1): 194-202.
[9] 刘阳, 郜志英, 周晓敏, 等. 工业数据驱动下薄板冷轧颤振的LSTM智能预报[J]. 机械工程学报, 2020, 56(11): 121-131.
LIU Y, GAO Z Y, ZHOU X M, et al.Industrial data-driven intelligent forecast for chatter of cold rolling of thin strip with LSTM recurrent neural network[J]. Journal of mechanical engineering, 2020, 56(11): 121-131.
[10] 王俊, 李霞, 周昔东, 等. 基于VMD和LSTM的超短期风速预测[J]. 电力系统保护与控制, 2020, 48(11): 45-52.
WANG J, LI X, ZHOU X D, et al.Ultra-short-term wind speed prediction based on VMD-LSTM[J]. Power system protection and control, 2020, 48(11): 45-52.
[11] PEI S Q, QIN H, ZHANG Z D, et al.Wind speed prediction method based on empirical wavelet transform and new cell update long short-term memory network[J]. Energy conversion and management, 2019, 196: 779-792.
[12] MORENO S R, SILVA R G D, MARIANI V C, et al. Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network[J]. Energy conversion and management, 2020, 213: 112869.
[13] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 62(3): 531-544.
[14] TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al.A complete ensemble empirical mode decomposition with adaptive noise[C]//2011 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP), Prague, Czech Republic, 2011, 4144-4147.
[15] 田秀娟, 于德新, 邢雪, 等. 交叉口短时流量CEEMDAN-PE-OSELM预测模型[J]. 哈尔滨工业大学学报, 2018, 50(3): 83-89.
TIAN X J, YU D X, XING X, et al.Prediction model of CEEMDAN-PE-OSELM for intersections short-term traffic flow[J]. Journal of Harbin Institute of Technology, 2018, 50(3): 83-89.
[16] HOCHREITER S, SCHMIDHUBER J.Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.
[17] MA X L, TAO Z M, WANG Y H, et al.Long short-term memory neural network for traffic speed prediction using remote microwave sensor data[J]. Transportation research part C: emerging technologies, 2015, 54: 187-197.