2025年4月8日 周二
首页 > 过刊浏览>2024年第39卷第5期 >163-171. DOI:10.19781/j.issn.1673-9140.2024.05.017
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基于PCA‑VMD‑MVO‑SVM的短期光伏输出功率预测方法
作者:
作者单位:

(1.长沙理工大学电气与信息工程学院，湖南 长沙 410114;2.电网防灾减灾全国重点实验室，湖南 长沙 410114;3.国网湖南省电力公司永州供电公司，湖南 永州 425000;4.湖南红太阳新能源科技有限公司，湖南 长沙 410111)

通讯作者:

赵 斌(1968—)，男，博士，教授，博士生导师，主要从事新能源科学技术及应用等方面的研究；E?mail：zhaobin19680507@163.com

中图分类号:

TM615

基金项目:

国家自然科学基金（52107071）;湖南省自然科学基金（2023JJ30048）;湖南省教育厅科学研究重点项目（22A0217）;长沙理工大学学术学位研究生项目（CSLGCX23155）


Prediction method of short‑term PV output power based on PCA‑VMD‑MVO‑SVM
Author:
Affiliation:

(1.College of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China; 2.State Key Laboratory of Disaster Prevention and Reduction for Power Grid, Changsha 410114, China; 3.Yongzhou Power Supply Company, State Grid Hunan Electric Power Co., Ltd., Yongzhou 425000, China; 4.Hunan Red Solar New Energy Science and Technology Co., Ltd., Changsha 410111, China)

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    摘要:

    为了提高光伏输出功率短期预测的准确性和可靠性，提出一种基于主成分分析法（principal component analysis, PCA）、变分模态分解法（variational mode decomposition, VMD）和多元宇宙算法（multi verse optimizer, MVO）对支持向量机（support vector machine, SVM）进行优化的光伏输出功率短期预测组合模型。先利用PCA具有的数据分析能力和VMD具有的数据分解性能，对多维训练数据进行降维和分解；再将提取后的数据输入由MVO算法优化的SVM预测模型，得到不同本征模态的光伏输出功率预测分量；最后，将各预测分量的结果进行叠加。研究结果表明：该模型在晴天、多云和阴雨天时的平均绝对百分比误差分别为0.745 3%、0.510 5%和1.015?6%。以多云天气为例，该模型的平均绝对百分比误差比MVO?SVM、VMD?MVO?SVM、PCA?MVO?SVM模型的分别降低了3.820 7%、2.917 3%和1.843 8%。

    Abstract:

    To enhance the accuracy and reliability of short-term photovoltaic (PV) output power forecasting, a hybrid model is proposed, which integrates principal component analysis (PCA), variational mode decomposition (VMD), and multi-verse optimizer (MVO) to optimize a support vector machine (SVM) for PV output power prediction. Initially, PCA's data analysis capabilities and VMD's data decomposition performance are leveraged to reduce the dimensionality and decompose the multidimensional training data. Subsequently, the extracted dataset is fed into an SVM prediction model optimized by the MVO algorithm to obtain PV output power forecast components for different intrinsic modes. Finally, the results of these forecast components are aggregated. The research findings indicate that the proposed model achieves mean absolute percentage errors (MAPEs) of 0.7453%, 0.5105%, and 1.0156% for sunny, partly cloudy, and rainy days, respectively. Taking partly cloudy weather as an example, the MAPE of the proposed model is reduced by 3.8207%, 2.9173%, and 1.8438% compared to the MVO-SVM, VMD-MVO-SVM, and PCA-MVO-SVM models, respectively.

    参考文献
    [1] 朱琼锋,李家腾,乔骥,等.人工智能技术在新能源功率预测的应用及展望[J].中国电机工程学报,2023,43(8):3027-3048. ZHU Qiongfeng,LI Jiateng,QIAO Ji,et al.Application and prospect of artificial intelligence technology in renewable energy forecasting[J].Proceedings of the CSEE,2023,43(8):3027-3048.
    [2] 王玉庆,徐飞,刘志坚,等.基于动态关联表征与图网络建模的分布式光伏超短期功率预测[J].电力系统自动化,2023,47(20):72-82. WANG Yuqing,XU Fei,LIU Zhijian,et al.Ultra-short-term power forecasting of distributed photovoltaic based on dynamic correlation characterization and graph network modeling[J].Automation of Electric Power Systems,2023,47(20):72-82.
    [3] GUO M,CAI S K.Impact of green innovation efficiency on carbon peak:carbon neutralization under environmental governance constraints[J].International Journal of Environmental Research and Public Health,2022,19(16):10245.
    [4] 朱显辉,于越,师楠,等.BP神经网络的分层优化研究及其在风电功率预测中的应用[J].高压电器,2022,58(2):158-163+170. ZHU Xianhui,YU Yue,SHI Nan,et al.Research on hierarchical optimization of BP neural network and its application in wind power prediction[J].High Voltage Apparatus,2022,58(2):158-163+170.
    [5] YANG D Z,WANG W T,GUEYMARD C A,et al.A review of solar forecasting,its dependence on atmospheric sciences and implications for grid integration:towards carbon neutrality[J].Renewable and Sustainable Energy Reviews,2022,161:112348.
    [6] 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.
    [7] MAYER M J,GRóF G.Extensive comparison of physical models for photovoltaic power forecasting[J].Applied Energy,2021,283:116239.
    [8] 王鹏翔,沈娟,王菁旸,等.基于PCA-LMD-WOA-ELM的短期光伏功率预测[J].智慧电力,2022,50(6):72-78. WANG Pengxiang,SHEN Juan,WANG Jingyang,et al.Short term photovoltaic power prediction based on PCA-LMD-WOA-ELM[J].Smart Power,2022,50(6):72-78.
    [9] 董志强,郑凌蔚,苏然,等.一种基于IGWO-SNN的光伏出力短期预测方法[J].电力系统保护与控制,2023,51(1):131-138. DONG Zhiqiang,ZHENG Lingwei,SU Ran,et al.An IGWO-SNN-based method for short-term forecast of photovoltaic output[J].Power System Protection and Control,2023,51(1):131-138.
    [10] 王清亮,杨博,应欣峰,等.非晴空条件下光伏发电短期功率预测方法[J].太阳能学报,2022,43(3):188-196. WANG Qingliang,YANG Bo,YING Xinfeng,et al.Short-term photovoltaic power forecasting method under non-clear sky condition[J].Acta Energiae Solaris Sinica,2022,43(3):188-196.
    [11] 宋玮琼,赵成,郭帅,等.考虑天气类型和历史相似日的短期光伏输出功率预测[J].电网与清洁能源,2023,39(2):75-82. SONG Weiqiong,ZHAO Cheng,GUO Shuai,et al.Short-term forecasting of photovoltaic output power considering weather type and historical similar days[J].Power System and Clean Energy,2023,39(2):75-82.
    [12] 魏铭琦,张天瑞,高秀秀,等.基于DA-RKELM算法的光伏发电功率预测方法[J].系统仿真学报,2020,32(10):2041-2051. WEI Mingqi,ZHANG Tianrui,GAO Xiuxiu,et al.A photovoltaic power forecasting method based on DA-RKELM algorithm[J].Journal of System Simulation,2020,32(10):2041-2051.
    [13] 文爽,马逸骋,孙志强.基于GWO-EEMD-BP神经网络的光伏发电功率短期预测[J].中南大学学报(自然科学版),2022,53(12):4799-4808. WEN Shuang,MA Yicheng,SUN Zhiqiang.Short-term prediction of photovoltaic power based on GWO-EEMD-BP[J].Journal of Central South University (Science and Technology),2022,53(12):4799-4808.
    [14] 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 Na,REN Qiang,LIU Guangchen,et al.PV power short-term forecasting method based on VMD-GWO-ELMAN[J].Electric Power,2022,55(5):57-65.
    [15] 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 Bing,ZHAN Zhongqiang,CHEN Jie,et al.Prediction research on short-term photovoltaic output based on pca-ga-Elman[J].Acta Energiae Solaris Sinica,2020,41(6):256-263.
    [16] 马骏,江锐,丁倩,等.基于多元宇宙优化支持向量机的短期光伏发电功率预测[J].热力发电,2020,49(4):87-92. MA Jun,JIANG Rui,DING Qian,et al.Prediction of short-term photovoltaic generation power based on HIMVO-SVM[J].Thermal Power Generation,2020,49(4):87-92.
    [17] 叶林,程文丁,李卓,等.光伏集群有功功率分层预测控制策略[J].电力系统自动化,2023,47(2):42-52. YE Lin,CHENG Wending,LI Zhuo,et al.Hierarchical prediction control strategy of active power for photovoltaic cluster[J].Automation of Electric Power Systems,2023,47(2):42-52.
    [18] 夏志凌,胡凯波,刘心悦,等.基于变模态分解的异步电机转子断条故障诊断[J].电工技术学报,2023,38(8):2048-2059. XIA Zhiling,HU Kaibo,LIU Xinyue,et al.Fault diagnosis of rotor broken bar in induction motor based on variable mode decomposition[J].Transactions of China Electrotechnical Society,2023,38(8):2048-2059.
    [19] 檀晓林,胡诗尧,张菁,等.基于主成分分析与加权TOPSIS的县域配电网投资效益评估方法[J].电力科学与技术学报,2022,37(3):133-139. TAN Xiaolin,HU Shiyao,ZHANG Jing,et al.Investment benefit assessment based on PCA and weighted TOPSIS in county distribution network[J].Journal of Electric Power Science and Technology,2022,37(3):133-139.
    [20] 邬程欢,贺民,龚明波,等.基于极限区间与改进PCA-SOM的电气运行参数对专变健康影响的量化评价方法[J].电力系统保护与控制,2021,49(17):101-108. WU Chenghuan,HE Min,GONG Mingbo,et al.Quantitative evaluation based on limit interval and improved PCA-SOM of electrical operating parameter health status of a special transformer[J].Power System Protection and Control,2021,49(17):101-108.
    [21] WANG X H,SUI G Z,XIANG J W,et al.Multi-domain extreme learning machine for bearing failure detection based on variational modal decomposition and approximate cyclic correntropy[J].IEEE Access,1809,8:197711-197729.
    [22] LI L L,WEN S Y,TSENG M L,et al.Renewable energy prediction:a novel short-term prediction model of photovoltaic output power[J].Journal of Cleaner Production,2019,228:359-375.
    [23] 武昕,严萌,郭一凡,等.基于结构化特征图谱的组合支持向量机非侵入式负荷辨识[J].电力系统自动化,2022,46(12):210-219. WU Xin,YAN Meng,GUO Yifan,et al.Non-intrusive load identification by combined support vector machine based on structured characteristic spectrum[J].Automation of Electric Power Systems,2022,46(12):210-219.
    [24] 林铮,刘可真,沈赋,等.考虑海上风电多机组时空特性的超短期功率预测模型[J].电力系统自动化,2022,46(23):59-66. LIN Zheng,LIU Kezhen,SHEN Fu,et al.Ultra-short-term power prediction model considering spatial-temporal characteristics of offshore wind turbines[J].Automation of Electric Power Systems,2022,46(23):59-66.
    [25] 刘雨佳,樊艳芳,白雪岩,等.基于特征交叉机制和误差补偿的风力发电功率短期预测[J].电工技术学报,2023,38(12):3277-3288. LIU Yujia,FAN Yanfang,BAI Xueyan,et al.Short-term wind power prediction based on feature crossover mechanism and error compensation[J].Transactions of China Electrotechnical Society,2023,38(12):3277-3288.
    [26] 陈瑶,陈晓宁.基于自适应Kmeans和LSTM 的短期光伏发电预测[J].电测与仪表,2023,60(7):94-99. Chen Yao,Chen Xiaoning.Prediction of short-term photovoltaic power generation based on adaptive Kmeans and LSTM[J].Electrical Measurement & Instrumentation,2023,60(7):94-99.
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引用本文

邹 港,赵 斌,罗 强,等.基于PCA‑VMD‑MVO‑SVM的短期光伏输出功率预测方法[J].电力科学与技术学报,2024,39(5):163-171.
ZOU Gang, ZHAO Bin, LUO Qiang, et al. Prediction method of short‑term PV output power based on PCA‑VMD‑MVO‑SVM[J]. Journal of Electric Power Science and Technology,2024,39(5):163-171.

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