基于SOM聚类和二次分解的BiGRU超短伏功率预测

董雪; 赵宏伟; 赵生校; 卢迪; 陈晓锋; 刘磊

doi:10.19912/j.0254-0096.tynxb.2021-0518

PDF(2321 KB)

太阳能学报 ›› 2022, Vol. 43 ›› Issue (11) : 85-93. DOI: 10.19912/j.0254-0096.tynxb.2021-0518

基于SOM聚类和二次分解的BiGRU超短伏功率预测

董雪^1~3, 赵宏伟³, 赵生校^1,2, 卢迪^1,2, 陈晓锋^1,2, 刘磊³

作者信息 +

ULTRA-SHORT-TERM FORECASTING METHOD OF PHOTOVOLTAIC POWER BASED ON SOM CLUSTERING, SECONDARY DECOMPOSITION AND BiGRU

Dong Xue^1~3, Zhao Hongwei³, Zhao Shengxiao^1,2, Lu Di^1,2, Chen Xiaofeng^1,2, Liu Lei³

Author information +

文章历史 +

摘要

提出一种基于自组织映射网络（SOM）聚类和二次分解的双向门限循环网络（BiGRU）超短期光伏功率预测方法。首先利用SOM聚类方法将输入数据进行天气分型聚类,以应对不同天气状态对光伏功率输出特性的影响;然后采用奇异谱分析和变分模态分解相结合的二次分解方法进行原始信号分解,减少信号的波动性,降低光伏数据特征映射的复杂度;最后将分解后的信号作为输入,采用BiGRU网络进行时序信息建模,有效结合不同时刻的信号特征,进一步提升功率预测的准确率。与其他几种经典方法相比,该文方法有效提升光伏功率预测的效果。

Abstract

A BiGRU ultra-short-term photovoltaic power forecasting method based on SOM clustering and secondary decomposition was proposed in this paper. To reduce the influence of different weather conditions on the characteristics of photovoltaic power output, SOM clustering was used to classify the input data. Then, a secondary decomposition method combining singular spectrum analysis and variational modal decomposition was adopted to decompose the original signal aiming to reduce the volatility of the original signal and the complexity of photovoltaic data feature mapping. Finally, the BiGRU network was built by time series modeling with the decomposed signal as input. The training strategy combined the signal characteristics at different times significantly improves the accuracy of the power prediction. Compared with several other classical methods, the proposed method can effectively improve the forecasting performance of photovoltaic power.

导出引用

董雪, 赵宏伟, 赵生校, 卢迪, 陈晓锋, 刘磊. 基于SOM聚类和二次分解的BiGRU超短伏功率预测[J]. 太阳能学报. 2022, 43(11): 85-93 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0518

Dong Xue, Zhao Hongwei, Zhao Shengxiao, Lu Di, Chen Xiaofeng, Liu Lei. ULTRA-SHORT-TERM FORECASTING METHOD OF PHOTOVOLTAIC POWER BASED ON SOM CLUSTERING, SECONDARY DECOMPOSITION AND BiGRU[J]. Acta Energiae Solaris Sinica. 2022, 43(11): 85-93 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0518

中图分类号： TM615

参考文献

[1] 荆博, 谭伦农, 钱政, 等. 光伏发电短期预测研究进展综述[J]. 电测与仪表, 2017, 54(12): 1-6.
JING B, TAN L N, QIAN Z, et al.An overview of research progress of short-term photovoltaic forecasts[J]. Electrical measurement & instrumentation, 2017, 54(12): 1-6.
[2] 习近平. 继往开来,开启全球应对气候变化新征程[N] .人民日报, 2020-12-13.
XI J P. Carrying on the past and opening up a new journey of global response to climate change[N]. People’s Daily, 2020-12-13.
[3] 龚莺飞, 鲁宗相, 乔颖, 等. 光伏功率预测技术[J]. 电力系统自动化, 2016, 40(4): 140-151.
GONG Y F, LU Z X, QIAO Y, et al.An overview of photovoltaic energy system output forecasting technology[J]. Automation of electric power systems, 2016, 40(4): 140-151.
[4] 谢小瑜, 周俊煌, 张勇军, 等. 基于W-BiLSTM的可再生能源超短期发电功率预测方法[J]. 电力系统自动化, 2021, 45(8): 175-184.
XIE X Y, ZHOU J H, ZHANG Y J, et al.W-BiLSTM based ultra-short-term generation power prediction method of renewable energy[J]. Automation of electric power systems, 2021, 45(8): 175-184.
[5] WANG K J, QI X X, LIU H D.A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network[J]. Applied energy, 2019, 251: 113315.
[6] SHARADGA H, HAJIMIRZA S, BALOG R S.Time series forecasting of solar power generation for large-scale photovoltaic plants[J]. Renewable energy, 2020, 150: 797-807.
[7] LI Q, ZHANG X Y, MA T J, et al.A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine[J]. Energy, 2021, 224: 120094.
[8] 姚宏民, 杜欣慧, 秦文萍. 基于密度峰值聚类及GRNN神经网络的光伏发电功率预测方法[J]. 太阳能学报, 2020, 41(9): 184-190.
YAO H M, DU X H, QIN W P.PV power forecasting approach based on density peaks clustering and general regression neural network[J]. Acta energiae solaris sinica, 2020, 41(9): 184-190.
[9] 叶林, 裴铭, 路朋, 等.基于天气分型的短期光伏功率组合预测方法[J]. 电力系统自动化, 2021, 45(1): 44-54.
YE L, PEI M, LU P, et al.Combination forecasting method of short-term photovoltaic power based on weather classification[J]. Automation of electric power systems, 2021, 45(1): 44-54.
[10] LIU D, SUN K J E. Random forest solar power forecast based on classification optimization[J]. Energy, 2019, 187: 115940.
[11] ISHII T, OTANI K, TAKASHIMA T, et al.Solar spectral influence on the performance of photovoltaic (PV) modules under fine weather and cloudy weather conditions[J]. Progress in photovoltaics: research and applications, 2013, 21(4): 481-489.
[12] ZHU H L, LI X, SUN Q, et al.A power prediction method for photovoltaic power plant based on wavelet decomposition and artificial neural networks[J]. Energies, 2015, 9(1):11.
[13] WANG L S, LIU Y H, LI T S, et al.Short-term PV power prediction based on optimized VMD and LSTM[J]. IEEE access, 2020, 8: 165849-165862.
[14] PRASAD R, ALI M, KWAN P, et al.Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation[J]. Applied energy, 2019, 236: 778-792.
[15] 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.Ultra-short-term photovoltaic power prediction of TD-BP neural network based on SSA and K-means[J]. Acta energiae solaris sinica, 2021, 42(4): 229-238.
[16] LI P T, ZHOU K L, LU X H, et al.A hybrid deep learning model for short-term PV power forecasting[J]. Applied energy, 2020, 259: 114216.
[17] 杨丽薇, 高晓清, 蒋俊霞, 等. 基于小波变换与神经网络的光伏电站短期功率预测[J]. 太阳能学报, 2020, 41(7): 152-157.
YANG L W, GAO X Q, JIANG J X, et al.Short-time photovoltaic output power prediction based on wavelet transform and neural network[J]. Acta energiae solaris sinica, 2020, 41(7): 152-157.
[18] LIU H, MI X W, LI Y F.Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network[J]. Energy conversion and management, 2018, 156: 498-514.
[19] WANG J J, LI Y N.Short-term wind speed prediction using signal preprocessing technique and evolutionary support vector regression[J]. Neural processing letters, 2018, 48: 1043-1061.
[20] KOHONEN T.Self-organizing maps[M]. Berlin: Springer Science & Business Media, 2012: 105-176.
[21] ELSNER J B, TSONIS A A.Singular spectrum analysis: a new tool in time series analysis[M]. Berlin: Springer Science & Business Media, 2013: 39-50.
[22] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2013, 62(3): 531-544.
[23] ZHANG Y C, LIU K P, QIN L, et al.Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods[J]. Energy conversion and management, 2016, 112: 208-219.
[24] LIMA F J, MARTINS F R, PEREIRA E B, et al.Forecast for surface solar irradiance at the Brazilian northeastern region using NWP model and artificial neural networks[J]. Renewable energy, 2016, 87: 807-818.
[25] SHAH A S B M, YOKOYAMA H, KAKIMOTO N. High-precision forecasting model of solar irradiance based on grid point value data analysis for an efficient photovoltaic system[J]. IEEE transactions on sustainable energy, 2015, 6(2): 474-481.
[26] 管霖, 赵琦, 周保荣, 等. 基于多尺度聚类分析的光伏功率特性建模及预测应用[J]. 电力系统自动化, 2018, 42(15): 24-30.
GUAN L, ZHAO Q, ZHOU B R, et al.Multi-scale clustering analysis based modeling of power characteristics and its application in prediction[J]. Automation of electric power systems, 2018, 42(15): 24-30.
[27] YU C J, LI Y L, ZHANG M J.An improved wavelet transform using singular spectrum analysis for wind speed forecasting based on Elman neural network[J]. Energy conversion and management, 2017, 148: 895-904.