DUAL-MODE DECOMPOSITION CNN-LSTM INTEGRATED SHORT-TERM WIND SPEED FORECASTING MODEL

Bi Guihong; Zhao Xin; Li Lu; Chen Shilong; Chen Chenpeng

doi:10.19912/j.0254-0096.tynxb.2021-1307

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (3) : 191-197. DOI: 10.19912/j.0254-0096.tynxb.2021-1307

DUAL-MODE DECOMPOSITION CNN-LSTM INTEGRATED SHORT-TERM WIND SPEED FORECASTING MODEL

Bi Guihong, Zhao Xin, Li Lu, Chen Shilong, Chen Chenpeng

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Abstract

This paper proposes a short-term wind power combination forecasting method based on two-mode decomposition,two-channel convolutional neural network (CNN) and long short-term memory neural network (LSTM) to improve the forecasting accuracy. Firstly, the wind speed time series clustered by the PAM method is decomposed into two modes of multi-scale subsequences by the singular spectral decomposition (SSD) and variational mode decomposition (VMD). The multi-scale subsequences of different modes can reduce the complexity and nonstationay of the original wind speed,and achieve the complementarity of the two modes.Secondly,the wind speed subsequences obtained by the two decomposition methods are combined into a matrix,which is input to the dual-channel CNN for waveform feature depth extraction. Finally,LSTM network is used to establish the historical wind speed time series dependency relationship,and the final wind speed prediction result is obtained on the basis of spatiotemporal correlation analysis. Experiment results show that the combined prediction model based on dual mode decomposition-dual channel CNN-LSTM can effectively improve the accuracy of short-term wind speed forecasting.

Key words

wind power / deep learning / convolutional neural networks / long short-term memory / singular spectrum decomposition / variational mode decomposition / wind speed forecasting

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Bi Guihong, Zhao Xin, Li Lu, Chen Shilong, Chen Chenpeng. DUAL-MODE DECOMPOSITION CNN-LSTM INTEGRATED SHORT-TERM WIND SPEED FORECASTING MODEL[J]. Acta Energiae Solaris Sinica. 2023, 44(3): 191-197 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1307

References

[1] 王伟胜, 王铮, 董存, 等. 中国短期风电功率预测技术现状与误差分析[J]. 电力系统自动化, 2021, 45(1): 17-27.
WANG W S, WANG Z, DONG C, et al.Status and error analysis of short-term forecasting technology of wind power in China[J]. Automation of electric power systems, 2021, 45(1): 7-27.
[2] 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.
[3] 林涛, 刘航鹏, 赵参参, 等. 基于SSA-PSO-ANFIS的短期风速预测研究[J]. 太阳能学报, 2021, 42(3): 128-134.
LIN T, LIU H P, ZHAO C C, et al.Short-term wind speed prediction based on SSA-PSO-ANFIS[J]. Acta energiae solaris sinica, 2021, 42(3): 128-134.
[4] 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.
[5] 殷豪, 欧祖宏, 陈德, 等. 基于二次模式分解和级联式深度学习的超短期风电功率预测[J]. 电网技术, 2020, 44(2): 445-453.
YIN H, OU Z H, CHEN D, et al.Ultra-short-term wind power prediction based on two-layer mode decomposition and cascaded deep learning[J]. Power system technology, 2020, 44(2): 445-453.
[6] 赵冬梅, 杜刚, 刘鑫, 等. 基于时序分解及机器学习的风电功率组合预测模型[J]. 现代电力, 2022, 39(1): 9-18.
ZHAO D M, DU G, LIU X, et al.Wind power combination prediction model based on time series decomposition and machine learning[J]. Modern electric power, 2022, 39(1): 9-18.
[7] DU W H,ZHOU J, WANG Z J, et al.Applicationof improved singular spectrum decomposition methofor composite fault diagnosis of gear boxes[J]. Sensors, 2018,18(11): 1-29.
[8] 史加荣, 赵丹梦, 王琳华, 等. 基于RR-VMD-LSTM的短期风电功率预测[J]. 电力系统保护与控制, 2021, 49(21): 63-70.
SHI J R, ZHAO D M, WANG L H, et al.Short-term wind power prediction based on RR-VMD-LSTM[J]. Power system protection and control, 2021, 49(21): 63-70.
[9] 许子非, 金江涛, 李春. 基于多尺度卷积神经网络的滚动轴承故障诊断方法[J]. 振动与冲击, 2021, 40(18): 212-220.
XU Z F, JIN J T, LI C.New method for the fault diagnosis of rolling bearings based on a multiscale convolutional neural network[J]. Journal of vibration and shock, 2021, 40(18): 212-220.
[10] 张凯, 林谷烨, 罗权. 改进LSTM神经网络在电机故障诊断的应用[J]. 计算机测量与控制, 2021, 29(4): 45-50.
ZHANG K, LIN G Y, LUO Q.Application of improved LSTM neural network in motor fault diagnosis[J]. Computer measurement and control, 2021, 29(4): 45-50.
[11] ZHI M, GUO F, HE G Y, et al.Milling tool wear state recognition based on partitioning around medoids (PAM) clustering[J]. International journal of advanced manufacturing technology, 2017, 88(5-8): 1203-1213.
[12] 邹臣嵩, 段桂芹. 基于改进K-medoids的聚类质量评价指标研究[J]. 计算机系统应用, 2019, 28(6): 235-242.
ZOU C S, DUAN G Q.Cluster quality evaluation index based on K-medoids algorithm[J]. Computer systems and applications, 2019, 28(6): 235-242.
[13] 王晓龙, 唐贵基, 何玉灵, 等. 变转速工况下基于改进奇异谱分解和1.5维包络阶次谱的风电机组轴承损伤识别[J]. 太阳能学报, 2021, 42(1): 240-247.
WANG X L, TANG G J, HE Y L, et al.Injury identification of wind turbine bearing based on ISSD and 1.5 dimension envelope order spectrum under variable rotating speed condition[J]. Acta energiae solaris sinica, 2021, 42(1): 240-247.
[14] 向玲, 邓泽奇, 赵玥. 基于LPF-VMD和KELM的风速多步预测模型[J]. 电网技术, 2019, 43(12): 4461-4467.
XIANG L, DENG Z Q, ZHAO Y.Multi-step wind speed prediction model based on LPF-VMD and KELM[J]. Power system technology, 2019, 43(12): 4461-4467.
[15] 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 transformand least squares support vector machines[J]. Acta energiae solaris sinica, 2021, 42(2): 97-103.
[16] NESHAT M, NEZHAD M M, ABBASNEJAD E, er al. A deep learning-based evolutionary model for short-term wind speed forecasting: a case study of the Lillgrund offshore wind farm[J]. Energy conversion and management, 2021, 236: 114002.