基于经验小波变换的光伏功率组合预测模型

doi:10.16182/j.issn1004731x.joss.21-FZ0709

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (11): 2627-2635.doi: 10.16182/j.issn1004731x.joss.21-FZ0709

基于经验小波变换的光伏功率组合预测模型

陈涛, 王艳, 纪志成

江南大学物联网技术应用教育部工程研究中心,江苏无锡 214122

收稿日期:2021-04-20 修回日期:2021-07-30 出版日期:2021-11-18 发布日期:2021-11-17
通讯作者: 王艳(1978-),女,博士,教授,研究方向为制造系统能效优化。E-mail：15850696098@163.com
作者简介:陈涛(1994-),男,硕士生,研究方向为智能算法与预测控制。E-mail：15850696098@163.com
基金资助:
国家重点研发计划(2018YFB170903); 国家自然科学基金(61973138)

Combination Forecasting Model of Photovoltaic Power Based on Empirical Wavelet Transform

Chen Tao, Wang Yan, Ji Zhicheng

Engineering Research Center for Internet of Things Technology Application Ministry of Education, Jiangnan University, Wuxi 214122, China

Received:2021-04-20 Revised:2021-07-30 Online:2021-11-18 Published:2021-11-17

摘要/Abstract

摘要： 为提高短期光伏功率的预测精度,提出一种基于经验小波变换(Empirical Wavelet Transform,EWT)和粒子群算法(Particle Swarm Optimization,PSO)优化随机森林(Random Forest,RF)的变权组合预测模型。利用灰色关联分析选出相似日,使用EWT将功率时间序列分解成不同频率的子模态,根据频率将其重构为高、中、低频3个模态,建立PSO-RF,PSO-BP,PSO-LSSVM预测模型动态计算各自权值进行重构,进行误差校正输出预测结果。通过对澳大利亚光伏电站输出功率进行预测,结果验证了EWT-PSO-RF组合模型的有效性。

关键词: 光伏功率预测, 经验小波变换, 随机森林, 灰色关联度, 误差校正

Abstract: In order to improve the prediction accuracy of short-term photovoltaic power, a variable weight combined prediction model based on Empirical Wavelet Transform (EWT) and PSO-optimized random forest(RF) is proposed. Gray correlation analysis is used to select similar days, EWT is used to decompose the power time series into sub-modes of different frequencies, and three modes of high, medium, and low frequency are reconstructed according to the frequency, PSO-RF and PSO-BP and PSO-LSSVM prediction models are established to dynamically calculate their respective weights for reconstruction, and error correction is performed to output the prediction results. By predicting the output power of Australian photovoltaic power stations, the results verify the effectiveness of the EWT-PSO-RF combined model, which effectively improves the accuracy of ultra-short-term photovoltaic power prediction.

Key words: PV power prediction, empirical wavelet transform, random forest, grey correlation degree, model parameter optimization

中图分类号:

TP391.9

陈涛, 王艳, 纪志成. 基于经验小波变换的光伏功率组合预测模型[J]. 系统仿真学报, 2021, 33(11): 2627-2635.

Chen Tao, Wang Yan, Ji Zhicheng. Combination Forecasting Model of Photovoltaic Power Based on Empirical Wavelet Transform[J]. Journal of System Simulation, 2021, 33(11): 2627-2635.

参考文献

[1] 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 Yingfei, Lu Zongxiang, Qiao Ying, et al.An Review of Photovoltaic Energy System Output Forecasting Technology[J]. Automation of Electric Power Systems, 2016, 40(4): 140-151.
[2] 丁明, 王伟胜, 王秀丽, 等. 大规模光伏发电对电力系统影响综述[J]. 中国电机工程学报, 2014, 34(1): 1-14.
Ding Ming, Wang Weisheng, Wang Xiuli, et al.A Review on the Effect of Large-scale PV Generation on Power Systems[J]. Proceedings of the CSEE, 2014, 34(1): 1-14
[3] 杨国清, 张凯, 王德意, 等. 基于包络线聚类的多模融合超短期光伏功率预测算法[J]. 电力自动化设备, 2021, 1(2): 1-8.
Yang Guoqing, Zhang Kai, Wang Deyi, et al.Multi-mode Fusion Ultra-short-term Photovoltaic Power Prediction Algorithm Based on Envelop Clustering[J]. Electric Power Automation Equipment, 2021, 1(2): 1-8.
[4] 许彪, 徐青山, 黄煜, 等. 基于藤copula分位数回归的光伏功率日前概率预测[J]. 电网技术2021, 1(1): 1-13.
Xu Biao, Xu Qingshan, Huang Yu, et al.Prediction of PV Power Day-ahead Probability Based on Vine Copula Quantile Regression[J]. Power Grid Technology, 2021, 1(1): 1-13
[5] Antonanzas J, Osorio N, Escobar R, et al.Review of Photovoltaic Power Forecasting[J]. Solar Energy (S0038-092X), 2018, 136(5): 78-111.
[6] 程泽, 李思宇, 韩丽洁, 等. 基于数据挖掘的光伏阵列发电预测方法研究[J]. 太阳能学报, 2017, 38(3): 726-733.
Cheng Ze, Li Siyu, Han Lijie, et al.Research on the Prediction Method of Photovoltaic Array Power Generation based on Data Mining[J]. Journal of Solar Energy, 2017, 38(3): 726-733.
[7] Dolara A, Grimaccia F, Leva S, et al.Aphysicalhy Brid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output[J]. Energies (S1996-1073), 2015, 8(2): 1138-1153.
[8] Widodo A, Shim M, Caesarendra W, et al.Intelligent Prognostics for Battery Health Monitoring based on Sample Entropy[J]. Expert System With Applications (S0957-4174), 2011, 38(9): 11763-11769.
[9] 张彩庆, 郑强. SKBA-LSSVM短期光伏发电功率预测模型[J]. 电力系统及其自动化学报, 2019, 31(8): 86-93.
Zhang Caiqing, Zheng Qiang.Short-term PV Power Prediction Model of SKBA-LSSVM[J]. Journal of Power Systems and Automation, 2019, 31(8): 86-93
[10] 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 Su, Jiang Xing, Zeng Liang, et al.Prediction of Ultra-short Term Photovoltaic Power based on VMD-DESN-MSGP Model[J]. Power Grid Technology, 2020, 44(3): 917-926.
[11] Chen C, Duan S, Cai T, et al.Online24-hsolar Power Forecasting based on Weather Type Classification Using Artificial Neural Network[J]. Solar Energy (S0038-092X), 2011, 85(11): 2856-2870.
[12] Mora-lopez L, Martinez-marchena I, Ppiliougine M, et al. Machine Learning Approach for Next Day Energy Production Forecasting Rid Connected Photovoltaic Plants[J]. Environmental Modelling and Software, 2005, 20(6):753-760.
[13] Shi J, Lee W J, Liu Y, et al. Forecasting Power Output of Photovoltaic System based on Weather Classification and Support Vector Machine[J]. IEEE Transactions on Industry Applications.2012.48(3):1064-1-69.
[14] 张金金, 张倩, 马愿, 等. 基于改进的随机森林和密度聚类的短期负荷频域预测方法[J]. 控制理论与应用, 2020, 37(10): 2257-2265.
Zhang Jinjin, Zhang Qian, Ma Yuan, et al.Short-term Load Frequency Domain Forecasting Method based on Improved Random Forest and Density Clustering[J]. Control Theory and Applications, 2020, 37(10): 2257-2265.
[15] Daubechies I, Lu J, Wu H T.Synchrosqueezed Wavelet Transforms: An Empirical Mode Decomposition-like Tool[J]. Applied and Computational Harmonic Analysis (S1063-5203), 2011, 30(2): 243-261.
[16] Daubechies I.Ten Lectures on Wavelets[J].USA: Society for Industrial and Applied Mathematics (S0916-7005), 1992, 21(5): 126-134.
[17] Breiman L.Random Forests[M]. Statistics Department. University of California: Machine Learning, 2001.
[18] Everitt B S.Classification and Regression Trees[M] Encyclopedia of Statistics in Behavioral Science. Cornell University, USA: John Wiley & Sons, Ltd, 2005.
[19] 梁彩霞, 高赵亮. 基于相似日和GA-DBN神经网络的光伏发电短期功率预测[J]. 电气应用, 2019, 38(3): 97-102.
Liang Caixia, Gao Zhaoliang.Short-term Power Prediction of Photovoltaic Power Generation based on Similar Days and GA-DBN Neural Network[J]. Electrical Application, 2019, 38(3): 97-102.

基于经验小波变换的光伏功率组合预测模型

Combination Forecasting Model of Photovoltaic Power Based on Empirical Wavelet Transform

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