基于PAM-SSD-LSTM的短期风速预测

赵鑫; 陈臣鹏; 毕贵红; 陈仕龙

doi:10.19912/j.0254-0096.tynxb.2021-0900

PDF(2628 KB)

太阳能学报 ›› 2023, Vol. 44 ›› Issue (1) : 281-288. DOI: 10.19912/j.0254-0096.tynxb.2021-0900

基于PAM-SSD-LSTM的短期风速预测

赵鑫, 陈臣鹏, 毕贵红, 陈仕龙

作者信息 +

SHORT-TERM WIND SPEED PREDICTION BASED ON PAM-SSD-LSTM

Zhao Xin, Chen Chenpeng, Bi Guihong, Chen Shilong

Author information +

文章历史 +

摘要

为提高短期风速预测的准确性,提出一种基于PAM聚类、奇异谱分解（SSD）和LSTM神经网络的组合预测模型来预测短期风速,以解决上述问题。首先,为提高神经网络的学习效率,采用PAM算法对原始风速数据进行相似日聚类;其次,SSD具有抑制模态混叠和虚假分量产生的优点,使用SSD分解风速序列,提取多尺度规律;最后,由于LSTM神经网络捕捉长时间依赖的序列的波动规律的能力较强,使用LSTM神经网络对分解后的风速分量进行预测,将各分量预测值叠加得到最终预测结果。实验结果表明,基于PAM-SSD-LSTM的组合预测模型可有效提高风速短期预测的准确率。

Abstract

Wind speed has characteristics of non-linearity,non-stationarity,intermittent and randomness. In order to improve the accuracy of short-term wind speed prediction,this paper proposes a combination prediction model based on PAM clustering, singular spectrum decomposition （SSD） and LSTM neural network to predict short-term wind speed to solve the problems mentioned above. Firstly, PAM algorithm is used to perform similar daily clustering on the original wind speed data to improve the learning efficiency of the neural network. Secondly,with the advantage of suppressing model aliasing and false components, SSD is used to decompose the wind speed series and extract multi-scale regular pattern. Finally, with a strong ability to capture the fluctuation law of long-term dependent series, LSTM neural network is used to predict the decomposed wind speed components. The prediction results of each component are accumulated to obtain the wind speed prediction results. Experiment results show that the combined prediction model based on PAM-SSD-LSTM can effectively improve the accuracy of short-term wind speed prediction.

导出引用

赵鑫, 陈臣鹏, 毕贵红, 陈仕龙. 基于PAM-SSD-LSTM的短期风速预测[J]. 太阳能学报. 2023, 44(1): 281-288 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0900

Zhao Xin, Chen Chenpeng, Bi Guihong, Chen Shilong. SHORT-TERM WIND SPEED PREDICTION BASED ON PAM-SSD-LSTM[J]. Acta Energiae Solaris Sinica. 2023, 44(1): 281-288 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0900

中图分类号： TM614

参考文献

[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.Current status and error analysis of short-term wind power forecasting technology in China[J]. Automation of electric power systems, 2021, 45(1): 17-27.
[2] 金吉, 王斌, 喻敏, 等. Lorenz方程优化EMD分解过程的短期风速预测[J]. 太阳能学报, 2021, 42(6): 342-348.
JIN J, WANG B, YU M, et al.Short-term wind speed prediction based on EMD optimized by Lorenz equation[J]. Acta energiae solaris sinica, 2021, 42(6): 342-348.
[3] 祝晓燕, 张金会, 付士鹏, 等. 基于集合经验模态分解和支持向量机的短期风速预测模型[J]. 华北电力大学学报(自然科学版), 2013, 40(5): 60-64.
ZHU X Y, ZHANG J H, FU S P, et al.Short-term wind speed prediction model based on EEMD and SVM[J]. Journal of North China Electric Power University(natural science edition), 2013, 40(5): 60-64.
[4] 魏炘, 石强, 符文熹, 等. 考虑CEEMDAN样本熵和SVR的短期风速预测[J]. 水电能源科学, 2020, 38(11): 207-210.
WEI X, SHI Q, FU W X, et al.Short-term wind speed prediction with CEEMDAN sample entropy and SVR[J]. Hydropower energy science, 2020, 38(11): 207-210.
[5] 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.
[6] 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.
[7] 唐贵基, 李楠楠, 王晓龙. 综合改进奇异谱分解和奇异值分解的齿轮故障特征提取方法[J]. 中国机械工程, 2020, 31(24): 2988-2996.
TANG G J, LI N N, WANG X L.Fault feature extraction method for gears based on ISSD and SVD[J]. China mechanical engineering, 2020, 31(24): 2988-2996.
[8] DU W H, ZHOU J, WANG Z J, et al.Application of improved singular spectrum decomposition method for composite fault diagnosis of gear boxes[J]. Sensors, 2018, 18(11): 1-29.
[9] 刘建华, 王进, 杨洪春, 等. 基于ACO-PAM综合算法的电力负荷聚类分析[J]. 电力科学与技术学报, 2011, 26(4): 94-99.
LIU J H, WANG J, YANG H C, et al.Power load clustering analysis based on ACO-PAM comprehensive algorithm[J]. Journal of electric power science and technology, 2011, 26(4): 94-99.
[10] 杨丽, 吴雨茜, 王俊丽, 等. 循环神经网络研究综述[J]. 计算机应用, 2018, 38(S2): 1-6,26.
YANG L, WU Y Q, WANG J L, et al.Research on recurrent neural network[J]. Journal of computer applications, 2018, 38(S2): 1-6,26.
[11] 王燕飞. 基于PAM聚类分析方法的路径优化算法研究实现[J]. 山西科技, 2013, 28(1): 85-88.
WANG Y F.Study on and Implementation of path optimization algorithm based on PAM cluster analysis[J]. Shanxi science and technology, 2013, 28(1): 85-88.
[12] 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.
[13] 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.
[14] RICHMAN J S, MOORMAN J R.Physiological time-series analysis using approximate entropay and sample entropy[J]. American journal of physiology heat and circulatory physiology, 2000, 278(6): 2039-2049.
[15] 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.
[16] 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 applications, 2019, 28(6): 235-242.
[17] 黄雨薇, 彭道刚, 姚峻, 等. 基于SSA和K均值的TD-BP神经网络超短期光伏功率预测[J].太阳能学报, 2021, 42(4): 229-238.
HUANG Y W, PENG D G, YAO J, et al.Utra-short-term photovoltaic power forecast of TD-BP neural network based on SSA and K-means[J]. Acta energiae solaris sinica, 2021, 42(4): 229-238.
[18] 吴俊利, 张步涵, 王魁. 基于Adaboost的BP神经网络改进算法在短期风速预测中的应用[J]. 电网技术, 2012, 36(9): 221-225.
WU J L, ZHANG B H, WANG K.Application of Adaboost-based BP neural network for short-term wind speed forecast[J]. Power system technology, 2012, 36(9): 221-225.
[19] 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(15): 114002.
[20] 李应求, 安勃, 李恒通. 基于NARX及混沌支持向量机的短期风速预测[J]. 电力系统保护与控制, 2019, 47(23): 65-73.
LI Y Q, AN B, LI H T.Short-term wind speed prediction based on NARX and chaos-support vector machine[J]. Power system protection and control, 2019, 47(23): 65-73.
[21] VASSALLO D, KRISHNAMURTHY R, FERNANDO H J S. Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error[J]. Wind energy science, 2021, 6(1): 295-309.