逆向云灰色关联相似日的EEMD-RL-GWO-LSTM区域风光功率短期预测

张宇华; 时鑫洋; 颜楠楠; 王育飞; 薛花

doi:10.19912/j.0254-0096.tynxb.2023-0887

PDF(1440 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (10) : 144-152. DOI: 10.19912/j.0254-0096.tynxb.2023-0887

逆向云灰色关联相似日的EEMD-RL-GWO-LSTM区域风光功率短期预测

张宇华¹, 时鑫洋¹, 颜楠楠^2,3, 王育飞¹, 薛花¹

作者信息 +

SHORT-TERM PREDICTION OF REGIONAL WIND-SOLAR POWER OF EEMD-RL-GWO-LSTM ON REVERSE CLOUD GREY CORRELATION SIMILAR DAYS

Zhang Yuhua¹, Shi Xinyang¹, Yan Nannan^2,3, Wang Yufei¹, Xue Hua¹

Author information +

文章历史 +

摘要

针对现有方法在风光预测时气象因素考虑不全面且未考虑风光功率关联性的问题,提出一种风光功率短期预测方法。首先,以云模型表征风光出力不确定性,逆向云结合灰色关联度分析不同气象特征对输出功率的影响程度,并设立选取标准及综合评分指标;其次,采用集合经验模态分解（EEMD）将选取相似日的功率数据分解为子序列;最后,将子序列和气象数据作为基于折射学习策略（RL）的灰狼算法（GWO）优化的改进长短期记忆网络（LSTM）模型的预测输入进行训练,对待预测日的子序列分别预测,并叠加得到短期区域风光发电功率的预测。以中国西北某风光联合电场数据为例,对该模型进行验证,结果表明,相比于现有预测模型,该文所提方法考虑了天气因素,具有较高的预测精度,能够较好地为区域风光联合电场的功率预测提供参考。

Abstract

Aiming at the problems of incomplete consideration of meteorological factors and non-consideration of wind-solar power correlation in wind-solar power prediction by existing methods, a short-term prediction method of wind-solar power is proposed. Firstly, the cloud model is used to characterize the uncertainty of wind-solar output, and the influence of different meteorological characteristics on the output power is analyzed by the reverse cloud combined with the grey correlation degree, and the selection criteria and comprehensive scoring index are set up. Secondly, the power data of similar days are decomposed into subsequences by ensemble empirical mode decomposition （EEMD）; Finally, the sub-sequences and meteorological data are trained as the forecast inputs of the improved long and short term memory network （LSTM） model optimized by the grey wolf algorithm （GWO） based on refraction learning strategy （RL）. The sub-sequences of the forecast days are predicted separately and superimposed to obtain the prediction of the short-term regional wind-solar power. The designed model is verified by the data of a certain wind-solar farm located in northwest China. The experimental results show that, compared with the existing prediction models, the proposed method takes into account the weather factor, has high prediction accuracy, and can better provide a reference for the power prediction of regional wind-scenic combined farms.

导出引用

张宇华, 时鑫洋, 颜楠楠, 王育飞, 薛花. 逆向云灰色关联相似日的EEMD-RL-GWO-LSTM区域风光功率短期预测[J]. 太阳能学报. 2024, 45(10): 144-152 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0887

Zhang Yuhua, Shi Xinyang, Yan Nannan, Wang Yufei, Xue Hua. SHORT-TERM PREDICTION OF REGIONAL WIND-SOLAR POWER OF EEMD-RL-GWO-LSTM ON REVERSE CLOUD GREY CORRELATION SIMILAR DAYS[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 144-152 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0887

中图分类号： TM61

参考文献

[1] 孔小兵, 刘向杰, 韩梅. 风光互补发电系统的分级递阶分布式预测控制[J].中国科学: 信息科学, 2018, 48(10): 1316-1332.
KONG X B, LIU X J, HAN M.Hierarchical distributed model predictive control of hybrid wind and solar generation system[J]. Scientia sinica (informationis), 2018, 48(10): 1316-1332.
[2] 李晖, 高涵宇, 张艳, 等. 考虑相关性的大规模风光互补电网扩展规划[J]. 电网技术, 2018, 42(7): 2120-2127.
LI H, GAO H Y, ZHANG Y, et al.Expansion planning of large scale hybrid wind-photovoltaic transmission network considering correlation[J]. Power system technology, 2018, 42(7): 2120-2127.
[3] 叶林, 屈晓旭, 么艳香, 等. 风光水多能互补发电系统日内时间尺度运行特性分析[J]. 电力系统自动化, 2018, 42(4): 158-164.
YE L, QU X X, YAO Y X, et al.Analysis on intraday operation characteristics of hybrid wind-solar-hydro power generation system[J]. Automation of electric power systems, 2018, 42(4): 158-164.
[4] 杨茂, 冯帆. 基于马氏距离相似度量的光伏功率超短期预测方法的研究[J]. 可再生能源, 2021, 39(2): 175-181.
YANG M, FENG F.Ultra-short-term prediction of PV power based on similar days of Mahalanobis distance[J]. Renewable energy resources, 2021, 39(2): 175-181.
[5] 祖向荣, 田敏, 白焰. 基于模糊聚类与函数小波核回归的短期负荷预测方法[J]. 电力自动化设备, 2016, 36(10): 134-140, 165.
ZU X R, TIAN M, BAI Y.Short-term load forecasting based on fuzzy clustering and functional wavelet-kernel regression[J]. Electric power automation equipment, 2016, 36(10): 134-140, 165.
[6] 张大海, 孙锴, 和敬涵. 基于相似日与多模型融合的短期负荷预测[J]. 电网技术, 2023, 47(5): 1961-1970.
ZHANG D H, SUN K, HE J H.Short-term load forecasting based on similar day and multi model fusion[J]. Power system technology, 2023, 47(5): 1961-1970.
[7] 阎洁, 许成志, 刘永前, 等. 基于风速云模型相似日的短期风电功率预测方法[J]. 电力系统自动化, 2018, 42(6): 53-59.
YAN J, XU C Z, LIU Y Q, et al.Short-term wind power prediction method based on wind speed cloud model in similar day[J]. Automation of electric power systems, 2018, 42(6): 53-59.
[8] 孟安波, 陈嘉铭, 黎湛联, 等. 基于相似日理论和CSO-WGPR的短期光伏发电功率预测[J]. 高电压技术, 2021, 47(4): 1176-1184.
MENG A B, CHEN J M, LI Z L, et al.Short-term photovoltaic power generation prediction based on similar day theory and CSO-WGPR[J]. High voltage engineering, 2021, 47(4): 1176-1184.
[9] 孟洋洋, 卢继平, 孙华利, 等. 基于相似日和人工神经网络的风电功率短期预测[J]. 电网技术, 2010, 34(12): 163-167.
MENG Y Y, LU J P, SUN H L, et al.Short-term wind power forecasting based on similar days and artificial neural network[J]. Power system technology, 2010, 34(12): 163-167.
[10] TAN B, MA X, SHI Q H, et al.Ultra-short-term wind power forecasting based on improved LSTM[C]//2021 6th International Conference on Power and Renewable Energy (ICPRE). Shanghai, China, 2021: 1029-1033.
[11] 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.
[12] 周家亿, 赵双双, 王忠东, 等. 结合用户画像的DTW-MANN-FM分布式光伏短期出力预测模型[J]. 太阳能学报, 2023, 44(9): 187-193.
ZHOU J Y,ZHAO S S,WANG Z D, et al.DTW-MANN-FM model combined with user profile for distributed photovoltaic power short-term forecasting[J]. Acta energiae solaris sinica, 2023, 44(9): 187-193.
[13] 王晓霞, 俞敏, 冀明, 等. 基于气候相似性的SSA-CNN-LSTM的光伏功率组合预测[J]. 太阳能学报, 2023, 44(6): 275-283.
WANG X X, YU M, JI M, et al.Photovoltaic power combination forecasting based on climate similarity and SSA-CNN-LSTM[J]. Acta energiae solaris sinica, 2023, 44(6): 275-283.
[14] MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J]. Advances in engineering software, 2014, 69(3): 46-61.
[15] 孙荣富, 张涛, 和青, 等. 风电功率预测关键技术及应用综述[J].高电压技术, 2021, 47(4): 1129-1143.
SUN R F, ZHANG T, HE Q, et al.Review on key technologies and applications in wind power forecasting[J]. High voltage engineering, 2021, 47(4): 1129-1143.
[16] LI D Y, LIU C Y.Study on the universality of the normal cloud model[J]. Engineer and science of China, 2004, 6(8): 28-33.
[17] 张光卫, 李德毅, 李鹏, 等. 基于云模型的协同过滤推荐算法[J]. 软件学报, 2007, 18(10): 2403-2411.
ZHANG G W, LI D Y, LI P, et al.A collaborative filtering recommendation algorithm based on cloud model[J]. Journal of software, 2007, 18(10): 2403-2411.
[18] 刘思峰, 蔡华, 杨英杰, 等. 灰色关联分析模型研究进展[J].系统工程理论与实践, 2013, 33(8): 2041-2046.
LIU S F, CAI H, YANG Y J, et al.Advance in grey incidence analysis modelling[J]. Systems engineering theory & practice, 2013, 33(8): 2041-2046.
[19] 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, 5.
MAO M Q, GONG W J, ZHANG L C, et al.Short-term photovoltaic generation forecasting based on EEMD-SVM combined method[J]. Proceedings of the CSEE, 2013, 33(34): 17-24, 5.
[20] 张晓凤, 王秀英. 灰狼优化算法研究综述[J]. 计算机科学, 2019, 46(3): 30-38.
ZHANG X F, WANG X Y.Comprehensive review of grey wolf optimization algorithm[J]. Computer science, 2019, 46(3): 30-38.
[21] WEN L, WU T B, CAI S H, et al.A novel grey wolf optimizer algorithm with refraction learning[J]. IEEE access, 2019, 7(3): 57805-57819.
[22] 陈海鹏, 周越豪, 王趁录, 等. 基于改进的CNN-LSTM短期风功率预测的系统旋转备用经济性分析[J]. 高电压技术, 2022, 48(2): 439-448.
CHEN H P, ZHOU Y H, WANG C L, et al.Economic analysis of system spinning reserve based on improved CNN-LSTM short term wind power prediction[J]. High voltage engineering, 2022, 48(2): 439-448.