基于灰色关联理论与经验模态分解的光伏出力超短期预测

doi:10.11930/j.issn.1004-9649.201707137

中国电力 ›› 2017, Vol. 50 ›› Issue (12): 159-164.DOI: 10.11930/j.issn.1004-9649.201707137

基于灰色关联理论与经验模态分解的光伏出力超短期预测

索春梅¹, 孙健², 张宗峰³, 王贤宗⁴

1. 哈尔滨电力职业技术学院,黑龙江哈尔滨 150030;
2. 国网北京市电力公司电力科学研究院,北京 100075;
3. 国网山东省电力公司日照供电公司,山东日照 276826;
4. 国网山东省电力公司临沂供电公司,山东临沂 276000

收稿日期:2017-07-20 出版日期:2017-12-20 发布日期:2018-01-30
作者简介:索春梅（1978—）,女,黑龙江哈尔滨人,副教授,从事新能源发电技术研究。E-mail:349103182@qq.com
基金资助:
国家电网公司科技项目（520201150012）

The Ultra-Short-Term Forecast of Photovoltaic Power Output Based on Grey Relational Analysis and Empirical Mode Decomposition

SUO Chunmei¹, SUN Jian², ZHANG Zongfeng³, WANG Xianzong⁴

1. Harbin Electric Power Vocational Technology College, Harbin 150030, China;
2. Beijing Electric Power Research Institute, Beijing 100075, China;
3. State Grid Shandong Rizhao Power Supply Company, Rizhao 276826, China;
4. State Grid Shandong Linyi Power Supply Company, Linyi 276000, China

Received:2017-07-20 Online:2017-12-20 Published:2018-01-30
Supported by:
This work is supported by Science and Technology Project of SGCC (No. 520201150012).

摘要/Abstract

摘要： 光伏出力受太阳辐照度、温度、瞬时云团等影响,出力序列呈现明显的非平稳特性,给预测工作带来难度。为降低出力波动性对预测效果的影响,满足光伏出力超短期预测对预测精度的需要,提出两阶段构建输入样本的方法,首先使用灰色关联度理论构建待预测日的相似日样本集合,再使用经验模态分解法拆解相似日历史出力,得到振动模态相对平稳的本征模函数及剩余分量后,使用支持向量机模型对每个分量进行滚动预测,最后将预测结果重构得到下一时刻的点预测值。实例证明,该组合模型的均方根误差可达到1.93,实现了较高精度,可以为光伏出力调度工作提供更多决策依据。

关键词: 灰色关联理论, 经验模态分解, 光伏, 超短期预测

Abstract: PV power output is affected by solar irradiance, temperature and instantaneous cloud, and its generation presents obvious non-stationary features, which increases the difficulty of prediction. To reduce the influence of output volatility on the prediction effectiveness and satisfy the accuracy requirement of ultra-short-term prediction, this paper proposes a two-stage method to build input samples. Firstly, the grey correlation principle is applied to build similar sample collection. Then, the empirical mode decomposition method is used to decompose the output sequence of similar days. After getting the relatively stable function and the remaining component, the support vector machine, a data mining tool, is used to do rolling prediction for every component. Finally, all the prediction results are added to get the prediction of next moment. A case study proves that the root-mean-square error of the combined model shows a high precision of 1.93, which can provide more decision-making support for PV power scheduling.

Key words: grey correlation principle, empirical mode decomposition method, photovoltaic, ultra-short-term prediction

中图分类号:

TM615

索春梅, 孙健, 张宗峰, 王贤宗. 基于灰色关联理论与经验模态分解的光伏出力超短期预测[J]. 中国电力, 2017, 50(12): 159-164.

SUO Chunmei, SUN Jian, ZHANG Zongfeng, WANG Xianzong. The Ultra-Short-Term Forecast of Photovoltaic Power Output Based on Grey Relational Analysis and Empirical Mode Decomposition[J]. Electric Power, 2017, 50(12): 159-164.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201707137

https://www.electricpower.com.cn/CN/Y2017/V50/I12/159

参考文献

[1] 国家能源局. 2016年光伏发电统计信息[EB/OL]. [2017-02-04]http://www.nea.gov.cn/2017-02/04/c_136030860.htm.
[2] 陈炜,艾欣,吴涛,等. 光伏并网发电系统对电网的影响研究综
述[J]. 电力自动化设备,2013,33（2）：26-32.
3 CHEN Wei, AI Xin, WU Tao, et al . Influence of grid-connected photovoltaic system on power network[J]. Electric Power Automation Equipment, 2013, 33(2): 26-32.
[3] 张雪莉,刘其辉,马会萌,等. 光伏电站输出功率影响因素分析[J]. 电网与清洁能源,2012,28（5）：76-81.
ZHANG Xueli, LIU Qihui, MA Huimeng, et al Analysis of influencingfactors of output power of photovoltaic power plant[J]. Power System and Clean Energy, 2012, 28(5): 76~81.
[4] 杨超颖,王金浩,王硕,等. 基于拟牛顿法小波神经网络的光伏发电系统短期功率预测模型[J]. 中国电力,2014,47（6）：117-124.
YANG Chaoying, WANG Jinhao, WANG Shuo, et al . A forecasting method of short-term power output of photovoltaic system based on wavelet neural network trained by quasi-newton nethod [J]. Electric Power, 2014, 47(6): 117-124.
[5] YONA A, SENJYU T, SABER A Y, et al . Application of neural network to 24-hour-ahead generating power forecasting for PV system[C]// IEEE Power and Energy Society General Meeting- Conversion and Delivery of Electrical Energy in the 21st Century. 2008.
[6] 栗然,李广敏. 基于支持向量机回归的光伏发电出力预测[J]. 中国电力,2008,41（2）：74-78.
LI Ran, LI Guangmin. Photovoltaic power generation output forecasting based on support vector machine regression technique[J]. Electric Power, 2008, 41(2): 74-78.
[7] 罗毅,邢校萄. 基于小波变换和支持向量机的短期光伏发电功率预测[J]. 新能源进展,2014,2（5）：380-384.
LUO Yi, XING Xiaotao. Short-termforecasting of photovoltaic power generation based on wavelet decomposition and support vector machine[J]. Advances in New and Renewable Energy, 2014, 2(5): 380-384.
[8] 朱永强,田军. 最小二乘支持向量机在光伏功率预测中的应用[J]. 电网技术,2011,35（7）：54-59.
ZHU Yongqiang, TIAN Jun. Application ofleast square support vector machine in photovoltaic power forecasting [J]. Power System Technology, 2011, 35(7): 54-59.
[9] 傅美平,马红伟,毛建容. 基于相似日和最小二乘支持向量机的光伏发电量短期预测[J]. 电力系统保护与控制,2012,40（16）：65-69.
FU Meiping, MA Hongwei, MAO Jianrong. Short-term photovoltaic power forecasting based on similar days and least square support vector machine[J]. Power System Protection and Control, 2012, 40(16): 65-69.
[10] 茆美琴,龚文剑,张榴晨,等. 基于EEMD-SVM方法的光伏电站短期出力预测[J]. 中国电机工程学报,2013,33（34）：17-24.
MAO Meiqin, GONG Wenjian, ZHANG Liuchen, et al . Short- term photovoltaic generation forecasting based on EEMD-SVM combined method [J]. Proceedings of the CSEE, 2013, 33(34): 17-24.
[11] 杜翠. 针对气象数据弱相关的光伏输出功率预测方法研究[D].北京：华北电力大学,2016.
[12] 刘思峰,郭天榜,党耀国,等.灰色系统理论及其应用[M]. 2版. 北京：科学出版社,1999：1-18.
[13] HYANG N E. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proc. R. Soc. Lond. A. 1998, 454(1971): 903-995.
[14] HUANG N E, SHEN Z, LONG S R, et al . A new view of nonlinear water waves: the Hillbert spectrum[J]. Annual Review of Fluid Mechanics, 1999, 31: 417-457.
[15] HASIMAH A, MUTHUSAMY H, SAZALI Y, et al . Facial emotion recognition using empirical mode decomposition[J]. Expert System with Application,2015, 42(3): 1261-1277.
[16] GUNTURKUN U. Bivariate empirical mode decomposition for cognitive radar scene analysis[J]. IEEE Signal Processing Letters,2015, 22(5): 603-607.
[17] TERRADAS J, OLIVER R, BALLESTER J L. Application of statistical techniques to the analysis of solar coronal oscillation[J]. The Astrophysical Journal, 2004(614): 435-447.
[18] VAPNIK V N. The nature of statistical learning theory[M]. Berlin: springer-verlag, 1995.

基于灰色关联理论与经验模态分解的光伏出力超短期预测

The Ultra-Short-Term Forecast of Photovoltaic Power Output Based on Grey Relational Analysis and Empirical Mode Decomposition

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	潘鹏程, 韩文舜, 郭雪丽. 基于有源和无源阻尼协同控制的光伏直流升压汇集系统谐振抑制[J]. 中国电力, 2025, 58(3): 20-30.
[2]	沃建栋, 潘武略, 李跃辉, 吴佳毅. 基于综合谐波驱动的光伏场站集电线路保护新方案[J]. 中国电力, 2025, 58(2): 133-139.
[3]	孙通, 张沈习, 曹毅, 曹佳晨, 程浩忠. 计及5G基站可调特性的配电网分布式光伏准入容量鲁棒优化[J]. 中国电力, 2025, 58(2): 140-146.
[4]	张玉敏, 张涌琛, 叶平峰, 吉兴全, 石春友, 蔡富东, 李一宸. 基于集合经验模态分解的增强核岭回归配电系统状态估计[J]. 中国电力, 2024, 57(9): 156-168.
[5]	周才期, 刘静利, 孙鹏凯, 张玉敏. 分布式光伏波动事件多级区间滚动预警方法[J]. 中国电力, 2024, 57(8): 96-107.
[6]	王海军, 居蓉蓉, 董颖华. 基于时空关联特征与B-LSTM模型的分布式光伏功率区间预测[J]. 中国电力, 2024, 57(7): 74-80.
[7]	胡满琳, 李楠, 李一鸣, 皮志勇, 朱益, 李振兴. 基于负序分量的含光伏电源配电网故障区段定位方法[J]. 中国电力, 2024, 57(5): 188-199.
[8]	刘韵艺, 汤渊, 苏盛, 吴裕宙, 王晓倩. 基于STL-Bayesian时空模型的分布式光伏系统异常检测[J]. 中国电力, 2024, 57(5): 222-231.
[9]	张宇, 魏新迟, 冯凯辉, 时珊珊, 孟子涵, 梁媛. 计及分布式光伏承载力的配电网侧独立储能充放电策略[J]. 中国电力, 2024, 57(4): 111-117.
[10]	战文华, 车建峰, 王勃, 丁禹. 基于网格化数值天气预报的区域光伏发电多输出功率预测方法[J]. 中国电力, 2024, 57(3): 144-151.
[11]	周于清, 李大虎, 姚伟, 宗启航, 周泓宇, 文劲宇. 受端近区光伏电站对LCC-HVDC系统稳定性影响分析[J]. 中国电力, 2024, 57(3): 170-182.
[12]	覃海, 陈胜, 张洪略, 夏天, 马覃峰, 段展宇, 颜伟. 并网光伏电站关口无功调节能力的分时段等值方法[J]. 中国电力, 2024, 57(2): 27-33.
[13]	杨国华, 祁鑫, 贾睿, 刘一峰, 蒙飞, 马鑫, 邢潇文. 基于CEEMD-SE的CNN&LSTM-GRU短期风电功率预测[J]. 中国电力, 2024, 57(2): 55-61.
[14]	刘宁, 张驰, 王栋, 杨明杰. 西北地区扶贫光伏整县开发决策演化博弈[J]. 中国电力, 2024, 57(2): 149-160.
[15]	李翰章, 冯江涛, 王鹏程, 荣澔洁, 柴宇唤. 基于TDE-SO-AWM-GRU的光伏发电功率预测模型[J]. 中国电力, 2024, 57(12): 41-49.

模态框（Modal）标题

基于灰色关联理论与经验模态分解的光伏出力超短期预测

The Ultra-Short-Term Forecast of Photovoltaic Power Output Based on Grey Relational Analysis and Empirical Mode Decomposition

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics