基于VMD和射箭算法优化改进ELM的短期光伏发电预测

陈龙, 张菁, 张昊立, 倪建辉, 高典

太阳能学报 ›› 2023, Vol. 44 ›› Issue (10) : 135-141.

PDF(1901 KB)
欢迎访问《太阳能学报》官方网站,今天是
PDF(1901 KB)
太阳能学报 ›› 2023, Vol. 44 ›› Issue (10) : 135-141. DOI: 10.19912/j.0254-0096.tynxb.2022-0843

基于VMD和射箭算法优化改进ELM的短期光伏发电预测

  • 陈龙, 张菁, 张昊立, 倪建辉, 高典
作者信息 +

SHORT-TERM PHOTOVOLTAIC POWER GENERATION FORECAST BASED ON VMD-IAA-IHEKLM MODEL

  • Chen Long, Zhang Jing, Zhang Haoli, Ni Jianhui, Gao Dian
Author information +
文章历史 +

摘要

为了提高光伏发电预测的准确性,提出一种结合变分模态分解(VMD)、改进的射箭算法(AA)和改进的极限学习机(ELM)的短期光伏功率预测模型。首先,将光伏数据进行变分模态分解;然后,利用混合核函数改进极限学习机;之后,利用随机反向学习策略改进射箭算法;最后,通过改进的射箭算法对混合核极限学习机中的核参数寻优并建立预测模型。通过对澳大利亚DKA太阳能中心的数据进行验证,证明该文方法的准确性。

Abstract

In order to improve the accuracy of photovoltaic power generation forecast, a short-term photovoltaic power generation forecast model based on variational mode decomposition(VMD), improved archery algorithm(IAA) and improved extreme learning machine(ELM) was proposed. Firstly,decompose the photovoltaic data by variational modal decomposition algorithm Secondly ,use hybrid kernel to improve extreme learning Machine,and then use the Random opposition-based learning to improve archery algorithm. Finally, the algorithm is used to optimize the kernel function parameters. The accuracy of this method is verified by the data of DKA solar energy center in Australia.

关键词

光伏发电 / 功率预测 / 机器学习 / 极限学习机 / 混合核函数 / 射箭算法

Key words

photovoltaic power / power forecasting / machine learning / extreme learning machine / hybrid kernel / archery algorithm

引用本文

导出引用
陈龙, 张菁, 张昊立, 倪建辉, 高典. 基于VMD和射箭算法优化改进ELM的短期光伏发电预测[J]. 太阳能学报. 2023, 44(10): 135-141 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0843
Chen Long, Zhang Jing, Zhang Haoli, Ni Jianhui, Gao Dian. SHORT-TERM PHOTOVOLTAIC POWER GENERATION FORECAST BASED ON VMD-IAA-IHEKLM MODEL[J]. Acta Energiae Solaris Sinica. 2023, 44(10): 135-141 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0843
中图分类号: TP391    TM615   

参考文献

[1] 赵书强, 张婷婷, 李志伟, 等. 基于数值特性聚类的日前光伏出力预测误差分布模型[J]. 电力系统自动化, 2019, 43(13): 36-45.
ZHAO S Q, ZHANG T T, LI Z W, et al.Distribution model of Day-ahead photovoltaic power forecasting error based on numerical characteristic clustering[J]. Automation of electric power systems, 2019, 43(13): 36-45.
[2] 王诚良, 朱凌志, 党东升, 等. 云团移动对光伏电站出力特性及系统调频的影响[J]. 可再生能源, 2017, 35(11): 1626-1631.
WANG C L, ZHU L Z, DANG D S, et al.Impacts on photovoltaic power characteristics and power system frequency regulation with cloud cluster movement[J]. Renewable energy resources, 2017, 35(11): 1626-1631.
[3] RAMSAMI P, OREE V.A hybrid method for forecasting the energy output of photovoltaic systems[J]. Energy conversion and management, 2015, 95: 406-413.
[4] HU K Y, CAO S H, WANG L D, et al.A new ultra-short term photovoltaic power prediction model based on ground -based cloud images[J]. Journal of cleaner production, 2018, 200: 731-745.
[5] ZANG H X, CHENG L L, DING T, et al.Hybrid method for short-term photovoltaic power forecasting based on deep convolutional neural network[J]. IET generation transmission & distribution, 2018, 12(20): 4557-4567.
[6] 冉成科, 夏向阳, 杨明圣, 等. 基于日类型及融合理论的BP网络光伏功率预测[J]. 中南大学学报(自然科学版), 2018, 49(9): 2232-2239.
RAN C K, XIA X Y, YANG M S, et al.BP network PV power forecast based on daily type and fusion theory[J]. Journal of Central South University (science and technology), 2018, 49(9): 2232-2239.
[7] 王粟, 江鑫, 曾亮, 等. 基于VMD-DESN-MSGP 模型的超短期光伏功率预测[J]. 电网技术, 2020, 44(3): 917-926.
WANG S, JIANG X, ZENG L, et al.Ultra-short-term photovoltaic power prediction based on VMD-DESN-MSGP model[J]. Power system technology, 2020, 44(3): 917-926.
[8] 屠亚南, 于艾清. 基于平抛模型的光伏多峰最大功率点预测跟踪方法[J]. 现代电力, 2019, 36(3): 27-33.
TU Y N, YU A Q.Photovoltaic multi-peak maximum power point predictive tracking method based on flat parabolic model[J]. Modern electric power, 2019, 36(3): 27-33.
[9] WANG J E, CAI L J, ZHAO X.Multiple-instance learning via an RBF kernel-based extreme learning machine[J]. Journal of intelligent systems, 2017, 26(1): 185-195.
[10] ZEIDABADI F A, DEHGHANI M, TROJOVSKY, et al. Archery algorithm: a novel stochastic optimization algorithm for solving optimization problems[J]. Computers, materials & continua, 2022, 72(1): 399-416.
[11] DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J]. IEEE transactions on signal processing, 2014, 62(3): 531-544.
[12] 姜智堯, 黄巍, 薛扬帆, 等. 屋顶机空调远程监测软硬件系统设计与故障诊断[J]. 制冷技术, 2021, 41(6): 15-20.
JIANG Z Y, HUANG W, XUE Y F, et al.Remote monitoring software and hardware system design and fault diagnosis of rooftop air conditioner[J]. Chinese journal of refrigeration technology, 2021, 41(6): 15-20.
[13] 吴松梅, 蒋建东, 燕跃豪, 等. 基于VMD-PSO-多核极限学习机的短期负荷预测[J]. 电力系统及其自动化学报,2022, 34(5): 18-25.
WU S M, JIANG J D, YAN Y H, et al.Short-term load forecasting based on VMD-PSO-MKELM method[J]. Proceedings of the CSU-EPSA, 2022, 34(5): 18-25.
[14] 范君, 王新, 徐慧. 粒子群优化混合核极限学习机的构造煤厚度预测方法[J]. 计算机应用, 2018, 38(6): 1820-1825, 1830.
FAN J, WANG X, XU H.Prediction method of tectonic deformed coal thickness based on particle swarm optimized and hybrid kernel extreme learning machine[J]. Journal of computer applications, 2018, 38(6): 1820-1825, 1830.
[15] LONG W, JIAO J J, LIANG X M, et al.A random opposition-based learning grey wolf optimizer[J]. IEEE access, 2019, 7: 113810-113825.
[16] 王小杨, 罗多, 孙韵琳, 等. 基于ABC-SVM和PSO-RF的光伏微电网日发电功率组合预测方法研究[J]. 太阳能学报, 2020, 41(3): 177-183.
WANG X Y, LUO D, SUN Y L, et al.Combined forecasting method of daily photovoltaic power generation in microgrid based on ABC-SVM and PSO-RF models[J]. Acta energiae solaris sinica, 2020, 41(3): 177-183.
[17] 胡兵, 詹仲强, 陈洁, 等. 基于PCA-GA-Elman的短期光伏出力预测研究[J] . 太阳能学报, 2020, 41(6): 256-263.
HU B, ZHAN Z Q, CHEN J, et al.Prediction research on short-term photovoltaic output based on PCA-GA-Elman[J]. Acta energiae solaris sinica, 2020, 41(6): 256-263.
[18] 张娜, 任强, 刘广忱, 等. 基于VMD-GWO-ELMAN的光伏功率短期预测方法[J]. 中国电力, 2022, 55(5):57-65.
ZHANG N, REN Q, LIU G C, et al.PV power short-term forecasting method based on VMD-GWO-ELMAN[J]. Electric power, 2022, 55(5): 57-65.

基金

国家自然科学基金(61803255)

PDF(1901 KB)

Accesses

Citation

Detail

段落导航
相关文章

/