基于综合距离相似的CEEMDAN-Informer光伏功率组合预测模型研究

詹莹; 王潇添; 王旭; 许野; 李薇; 王雯雯

doi:10.19912/j.0254-0096.tynxb.2024-0521

PDF(2213 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (8) : 315-326. DOI: 10.19912/j.0254-0096.tynxb.2024-0521

基于综合距离相似的CEEMDAN-Informer光伏功率组合预测模型研究

詹莹¹, 王潇添², 王旭¹, 许野¹, 李薇¹, 王雯雯¹

作者信息 +

STUDY ON PHOTOVOLTAIC POWER COMBINED PREDICTION MODEL EMPLOYING CEEMDAN-INFORMER BASED ON COMPREHENSIVE DISTANCE SIMILAR

Zhan Ying¹, Wang Xiaotian², Wang Xu¹, Xu Ye¹, Li Wei¹, Wang Wenwen¹

Author information +

文章历史 +

摘要

针对当前光伏发电功率预测相似日选取结果失真、信号分解质量较差和预测模型训练时间长且易陷入局部极值等缺陷导致的预测精度较差的问题,构建融合综合距离相似日选取、自适应噪声的完备集合经验模态分解和Informer算法光伏出力组合预测模型。在利用皮尔逊相关系数法完成关键气象因素筛选的基础上,使用灰色关联度、动态时间弯曲、欧氏距离和夹角余弦的加权组合的综合相似距离法选定待预测日的历史相似日,并利用自适应噪声完备集合经验模态分解（CEEMDAN）算法对历史出力序列进行分解,生成高质量的模型训练样本集,最后基于Informer算法构建光伏出力组合预测模型,实现对光伏出力的精确预测。通过在云南某光伏电站的实际应用,结果表明相较于其他组合预测模型,所提基于综合距离相似的CEEMDAN-Informer模型可捕捉光伏出力的骤变趋势,预测精度较高,可为光伏电站后续的优化调度奠定较好的基础。

Abstract

To address the issues of prediction in the selection of similar days, poor signal decomposition quality, long training times, and the tendency of prediction models to get stuck in local optima, leading to poor prediction accuracy in current photovoltaic power forecasting, this paper innovatively constructs a combined prediction model for PV output. This model integrates comprehensive distance-based similar day selection, complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN）, and the Informer algorithm. On the basis of using the Pearson correlation coefficient method to screen key meteorological factors, a comprehensive similar distance method, which combines grey relational analysis（GRA）, dynamic time warping（DTW）, Euclidean distance, and cosine similarity with weighted combinations, is employed to select historical similar days for the day to be predicted. The CEEMDAN algorithm is then utilized to decompose historical output sequences, generating high-quality training sample sets. Finally, an Informer-based combined prediction model is constructed to achieve accurate PV output forecasting. The application results from a PV power satation in Yunnan indicate that, compared to other combined prediction models, the proposed CEEMDAN-Informer model based on comprehensive distance similarity captures abrupt trends in PV output more effectively and offers higher prediction accuracy. This model provides a robust foundation for subsequent optimal scheduling of PV power satations.

导出引用

詹莹, 王潇添, 王旭, 许野, 李薇, 王雯雯. 基于综合距离相似的CEEMDAN-Informer光伏功率组合预测模型研究[J]. 太阳能学报. 2025, 46(8): 315-326 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0521

Zhan Ying, Wang Xiaotian, Wang Xu, Xu Ye, Li Wei, Wang Wenwen. STUDY ON PHOTOVOLTAIC POWER COMBINED PREDICTION MODEL EMPLOYING CEEMDAN-INFORMER BASED ON COMPREHENSIVE DISTANCE SIMILAR[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 315-326 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0521

中图分类号： TK615

参考文献

[1] XIE H N, GOH H H, ZHANG D D, et al.Eco-Energetical analysis of circular economy and community-based virtual power plants(CE-cVPP): a systems engineering-engaged life cycle assessment(SE-LCA) method for sustainable renewable energy development[J]. Applied energy, 2024, 365: 123191.
[2] 龙勇, 宋敏. 全球能源安全大变局下保障我国能源安全的思路与方略[J]. 改革, 2023(10): 74-83.
LONG Y, SONG M.Ideas and strategies for ensuring China's energy security in the context of global energy security upheaval[J]. Reform, 2023(10): 74-83.
[3] GE L J, DU T S, LI C L, et al.Virtual collection for distributed photovoltaic data: challenges, methodologies, and applications[J]. Energies, 2022, 15(23): 8783.
[4] CREUTZIG F, AGOSTON P, GOLDSCHMIDT J C, et al.The underestimated potential of solar energy to mitigate climate change[J]. Nature energy, 2017, 2: 17140.
[5] LI C Y, ZHU C H, WANG X N, et al.Green finance: how can it help Chinese power enterprises transition towards carbon neutrality[J]. Environmental science and pollution research international, 2023, 30(16): 46336-46354.
[6] YANG D Z, KLEISSL J, GUEYMARD C A, et al.History and trends in solar irradiance and PV power forecasting: a preliminary assessment and review using text mining[J]. Solar energy, 2018, 168: 60-101.
[7] 陈龙, 张菁, 张昊立, 等. 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.
CHEN L, ZHANG J, ZHANG H L, et al.Short-term photovoltaic power generation forecast based on VMD-IAA-IHEKLM model[J]. Acta energiae solaris sinica, 2023, 44(10): 135-141.
[8] SOBRI S, KOOHI-KAMALI S, RAHIM N A.Solar photovoltaic generation forecasting methods: a review[J]. Energy conversion and management, 2018, 156: 459-497.
[9] HAO Y, TIAN C S.A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting[J]. Applied energy, 2019, 238: 368-383.
[10] WANG H Z, LEI Z X, ZHANG X, et al.A review of deep learning for renewable energy forecasting[J]. Energy conversion and management, 2019, 198: 111799.
[11] MACIEL J N, LEDESMA J J G, JUNIOR O H A. Hybrid prediction method of solar irradiance applied to short-term photovoltaic energy generation[J]. Renewable and sustainable energy reviews, 2024, 192: 114185.
[12] HOU X X, JU C, WANG B.Prediction of solar irradiance using convolutional neural network and attention mechanism-based long short-term memory network based on similar day analysis and an attention mechanism[J]. Heliyon, 2023, 9(11): e21484.
[13] 叶林, 陈政, 赵永宁, 等. 基于遗传算法-模糊径向基神经网络的光伏发电功率预测模型[J]. 电力系统自动化, 2015, 39(16): 16-22.
YE L, CHEN Z, ZHAO Y N, et al.Photovoltaic power forecasting model based on genetic algorithm and fuzzy radial basis function neural network[J]. Automation of electric power systems, 2015, 39(16): 16-22.
[14] 薛阳, 李金星, 杨江天, 等. 基于相似日分析和改进鲸鱼算法优化LSTM网络模型的光伏功率短期预测[J].南方电网技术, 2023, 17: 1-9.
XUE Y, LI J X, YANG J T, et al.Short-Term prediction of photovoltaic power based on similar day analysis and improved whale algorithm to optimize LSTM network model[J]. Southern power system technology, 2023, 17: 1-9.
[15] 张程, 林谷青, 黄靖, 等. 基于AMBOA-DBN结合相似日的短期光伏功率预测[J]. 太阳能学报, 2023, 44(6): 290-299.
ZHANG C, LIN G Q, HUANG J, et al.Short-term pv power prediction based on AMBOA-DBN combined with similar days[J]. Acta energiae solaris sinica, 2023, 44(6): 290-299.
[16] 张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62-69.
ZHANG Y Q, CHENG Q Z, JIANG W J, et al.Photovoltaic power prediction model based on EMD-PCA-LSTM[J]. Acta energiae solaris sinica, 2021, 42(9): 62-69.
[17] WANG S J, LIU S, GUAN X.Ultra-short-term power prediction of a photovoltaic power station based on the VMD-CEEMDAN-LSTM model[J].Frontiers in energy research, 2022, 10: 945327.
[18] WANG H, SUN J B, WANG W J.Photovoltaic power forecasting based on EEMD and a variable-weight combination forecasting model[J]. Sustainability, 2018, 10(8): 2627.
[19] ZHANG N, REN Q, LIU G C, et al.Short-term PV output power forecasting based on CEEMDAN-AE-GRU[J]. Journal of electrical engineering & technology, 2022, 17(2): 1183-1194.
[20] 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.
[21] 姜景芮, 苗田, 彭婧, 等. 基于深度学习算法的光伏发电功率预测方法研究[J]. 自动化应用, 2023, 64(13): 102-104.
JIANG J R, MIAO T, PENG J, et al.Research on photovoltaic power prediction method based on deep learning algorithm[J]. Automation application, 2023, 64(13): 102-104.
[22] 史加荣, 殷诏. 基于GRU-BLS的超短期光伏发电功率预测[J]. 智慧电力, 2023, 51(9): 38-45.
SHI J R, YIN Z.Prediction of ultra short term photovoltaic power generation based on GRU-BLS[J]. Smart power, 2023, 51(9): 38-45.
[23] COLOMINAS M A, SCHLOTTHAUER G, TORRES M E.Improved complete ensemble EMD: a suitable tool for biomedical signal processing[J]. Biomedical signal processing and control, 2014, 14: 19-29.
[24] 庞博文, 丁月明, 杜善慧, 等. 基于CEEMDAN-BO-LSTNet的风电出力短期预测[J]. 电测与仪表, 2023, 60(9): 109-116, 170.
PANG B W, DING Y M, DU S H, et al.Short-term forecasting of wind power output based on CEEMDAN-BO-LSTNet[J]. Electrical measurement & instrumentation, 2023, 60(9): 109-116, 170.
[25] CHANG K M.Ensemble empirical mode decomposition for high frequency ECG noise reduction[J]. Biomedizinische technik biomedical engineering, 2010, 55(4): 193-201.
[26] WANG Y N, YUAN Z, LIU H Q, et al.A new scheme for probabilistic forecasting with an ensemble model based on CEEMDAN and AM-MCMC and its application in precipitation forecasting[J]. Expert systems with applications, 2022, 187: 115872.
[27] GONG M J, ZHAO Y, SUN J W, et al.Load forecasting of district heating system based on Informer[J]. Energy, 2022, 253: 124179.
[28] LIU F, DONG T, LIU Y.An improved informer model for short-term load forecasting by considering periodic property of load profiles[J]. Frontiers in energy research, 2022, 10: 950912.
[29] LI W Y, FU H Y, HAN Z Y, et al.Intelligent tool wear prediction based on Informer encoder and stacked bidirectional gated recurrent unit[J]. Robotics and computer-integrated manufacturing, 2022, 77: 102368.
[30] 朱莉, 韩凯萍, 朱春强. 二次分解策略组合Informer的短期电力负荷预测方法[J]. 国外电子测量技术, 2023, 42(6): 23-32.
ZHU L, HAN K P, ZHU C Q.Short-term power load forecasting method of two-level decomposition combines Informer[J]. Foreign electronic measurement technology, 2023, 42(6): 23-32.
[31] 张倩, 蒙飞, 李涛, 等. 基于周期信息增强的Informer光伏发电功率预测[J]. 中国电力, 2023, 56(10): 186-193.
ZHANG Q, MENG F, LI T, et al.Informer photovoltaic power generation forecasting based on cycle information enhancement[J]. Electric power, 2023, 56(10): 186-193.