PREDICTION OF PHOTOVOLTAIC POWER UNDER DIFFERENT SOLAR RADIATION BASED ON SECONDARY DECOMPOSITION

Wang Dewen; Jiao Tianyuan

doi:10.19912/j.0254-0096.tynxb.2023-0772

PDF(2409 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (9) : 360-368. DOI: 10.19912/j.0254-0096.tynxb.2023-0772

PREDICTION OF PHOTOVOLTAIC POWER UNDER DIFFERENT SOLAR RADIATION BASED ON SECONDARY DECOMPOSITION

Wang Dewen, Jiao Tianyuan¹

Author information +

History +

Abstract

A photovoltaic power prediction model based on quadratic decomposition and improved particle swarm optimization algorithm is proposed considering the impact of different solar radiation on photovoltaic power. Through Spearman and Kendall's correlation analysis of various meteorological factors affecting photovoltaic power, it was found that the correlation coefficients between total tilt radiation, total horizontal radiation, diffuse tilt radiation, diffuse horizontal radiation, and photovoltaic power are relatively large. Then we use CLARANS to divide the sample data into strong radiation, medium radiation and weak radiation according to the solar radiant intensity. For the three types of data, we use CEEMDAN to decompose the key meteorological factors and power twice, fully mining time series information and reducing data instability. The GWCPSO is proposed to optimize the hyperparameter of the convolutional neural network and the bidirectional long short-term memory network, improve the efficiency of parameter adjustment, and finally build a prediction model for photovoltaic power prediction. Analyzing the prediction errors of different decomposition methods and network models under three types of solar radiation, the results show that the proposed prediction model can effectively improve the prediction accuracy of photovoltaic power under different solar radiation conditions.

Key words

photovoltaic power prediction / secondary decomposition / particle swarm optimization / convolutional neural network / bidirectional long short-term memory

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Wang Dewen, Jiao Tianyuan. PREDICTION OF PHOTOVOLTAIC POWER UNDER DIFFERENT SOLAR RADIATION BASED ON SECONDARY DECOMPOSITION[J]. Acta Energiae Solaris Sinica. 2024, 45(9): 360-368 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0772

References

[1] 国家能源局. 新型电力系统发展蓝皮书(征求意见稿)[EB/OL].http://www.nea.gov.cn/2023-01/06/c_1310688702.htm.
National Energy Administration.Blue book on the development of new power systems(draft for comments)[EB/OL]. http://www.nea.gov.cn/2023-01/06/c_1310688702.htm.
[2] 张臻, 陈天鹏, 王磊, 等. 基于地基云图的超短期太阳辐照预测方法与装置研究[J]. 太阳能学报, 2023, 44(1): 133-140.
ZHANG Z, CHEN T P, WANG L, et al.Research on ultra-short-term solar irradiance prediction method and devicebased on ground-based cloud images[J]. Acta energiae solaris sinica, 2023, 44(1): 133-140.
[3] ZHENG J Q, ZHANG H R, DAI Y H, et al.Time series prediction for output of multi-region solar power plants[J]. Applied energy, 2020, 257: 114001.
[4] KORKMAZ D.SolarNet: a hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting[J]. Applied energy, 2021, 300: 117410.
[5] ELIZABETH MICHAEL N, HASAN S, AL-DURRA A, et al.Short-term solar irradiance forecasting based on a novel Bayesian optimized deep long short-term memory neural network[J]. Applied energy, 2022, 324: 119727.
[6] 向玲, 刘佳宁, 苏浩, 等. 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.
XIANG L, LIU J N, SU H, et al.Research on multi-step wind speed forecast based on ceemdan secondary decomposition and lstm[J]. Acta energiae solaris sinica, 2022, 43(8): 334-339.
[7] 董雪, 赵宏伟, 赵生校, 等. 基于SOM聚类和二次分解的BiGRU超短期光伏功率预测[J]. 太阳能学报, 2022, 43(11): 85-93.
DONG X, ZHAO H W, ZHAO S X, et al.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.
[8] 孟安波, 陈嘉铭, 黎湛联, 等. 基于相似日理论和CSO-WGPR的短期光伏发电功率预测[J]. 高电压技术, 2021, 47(4): 1176-1184.
MENG A B, CHEN J M, LI Z L, et al.Short-term photovoltaic power generation prediction based on similar day theory and CSO-WGPR[J]. High voltage engineering, 2021, 47(4): 1176-1184.
[9] YANG M, ZHAO M, HUANG D W, et al.A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder[J]. Renewable energy, 2022, 194: 659-673.
[10] ZHOU Y, ZHOU N R, GONG L H, et al.Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine[J]. Energy, 2020, 204: 117894.
[11] 王开艳, 杜浩东, 贾嵘, 等. 基于相似日聚类和QR-CNN-BiLSTM模型的光伏功率短期区间概率预测[J]. 高电压技术, 2022, 48(11): 4372-4388.
WANG K Y, DU H D, JIA R, et al.Short-term interval probability prediction of photovoltaic power based on similar daily clustering and QR-CNN-BiLSTM model[J]. High voltage engineering, 2022, 48(11): 4372-4388.
[12] NG R T, HAN J W.CLARANS: a method for clustering objects for spatial data mining[J]. IEEE transactions on knowledge and data engineering, 2002, 14(5): 1003-1016.
[13] HE D Q, LIU C Y, JIN Z Z, et al.Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning[J]. Energy, 2022, 239(PB): 122108.
[14] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 62(3): 531-544.
[15] 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.
[16] HOCHREITER S, SCHMIDHUBER J.Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.
[17] 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.
[18] 吴小刚, 刘宗歧, 田立亭, 等. 基于改进多目标粒子群算法的配电网储能选址定容[J]. 电网技术, 2014, 38(12): 3405-3411.
WU X G, LIU Z Q, TIAN L T, et al.Energy storage device locating and sizing for distribution network based on improved multi-objective particle swarm optimizer[J]. Power system technology, 2014, 38(12): 3405-3411.
[19] VAZIRI J, FARID D, NAZEMI ARDAKANI M, et al.A time-varying stock portfolio selection model based on optimized PSO- BiLSTM and multi-objective mathematical programming under budget constraints[J]. Neural computing and applications, 2023, 35(25): 18445-18470.