康文豪,徐天奇,王阳光,邓小亮,李琰

• 1:云南民族大学云南省高校电力信息物理融合系统重点实验室
• 2:国网湖南省电力有限公司

摘要(Abstract)：

为了解决风电功率的间歇性与非平稳性带来的功率预测难度，提出了一种基于CEEMDAN-精细复合多尺度熵和Stacking集成学习的短期风电功率预测方法。在对风电功率进行预测之前，对风电功率数据进行预处理。首先引入自适应噪声完备集合经验模态分解(CEEMDAN)方法分解风电功率原始序列，并计算各分解分量的精细复合多尺度熵(RCMSE)。然后，将熵值相近的分量序列重组成新序列，以降低模型复杂度和提高计算效率。在预测阶段，对重组之后的序列分别建立Stacking集成学习模型进行风电功率短期预测，最后对预测结果进行重组。通过新疆某风电场实测数据证明：结合各单一预测模型优点的Stacking集成学习模型方法与其4种基学习器KNN、RF、SVR和ANN相比，Stacking模型具有更高的风电预测准确度。在同等条件下，CEEMDAN-RCMSE-Stacking模型均方根误差相比单一的Stacking模型及EMD-RCMSE-Stacking模型分别减少了20.34%和9.74%,平均绝对误差分别减少了24.55%和6.35%,而拟合优度系数分别提高了4.09%和1.62%,即CEEMDAN-RCMSE-Stacking模型拥有更高的预测性能。

关键词(KeyWords)： 短期风电功率预测;CEEMDAN;精细复合多尺度熵;Stacking集成学习;影响因素;新能源;清洁可再生能源

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(61761049)

作者(Author): 康文豪,徐天奇,王阳光,邓小亮,李琰

DOI: 10.13928/j.cnki.wrahe.2022.02.016

参考文献(References)：

• [1] 袁智勇，白浩，黄安迪，等.基于 EEMD-SSA 和改进 ELM 的短期风电功率预测方法[J].水利水电技术(中英文),2021,52(S1):323-331.YUAN Zhiyong,BAI Hao,HUANG Andi,et al.Short-term wind power prediction method based on EEMD-SSA and improved ELM[J].Water Resources and Hydropower Engineering,2021,52(S1):323-331.
• [2] 周小麟，童晓阳.基于CEEMD-SBO-LSSVR的超短期风电功率组合预测[J].电网技术，2021,45(3):855-864.ZHOU Xiaolin,TONG Xiaoyang.Ultra-short-term wind power combined prediction based on CEEMD-SBO-LSSVR[J].Power System Technology,2021,45(3):855-864.
• [3] 郑海，陈湘萍，李慧，等.小波变换和RBF神经网络的风功率预测[J].实验室研究与探索，2019,38(10):36-39.ZHENG Hai,CHEN Xiangping,LI Hui,et al.Wind power prediction based on wavelet transform and RBF neural network[J].research and exploration in laboratory,2019,38(10),36-39.
• [4] 孙国良，伊力哈木·亚尔买买提，张宽，等.基于小波变换与IAGA-BP神经网络的短期风电功率预测[J/OL].电测与仪表：(2021-06-28)[2021-08-05].http://kns.cnki.net/kcms/detail/23.1202.TH.20210628.1347.002.html.SUN Guoliang,YILIHAMU·Yaermaimaiti,ZHANG Kuang,et al.Short-term predictionof wind power based on wavelet transformand IAGA-BPneural network[J/OL].Electrical Measurement & Instrumentation:(2021-06-28)[2021-08-05].http://kns.cnki.net/kcms/detail/23.1202.TH.20210628.1347.002.html.
• [5] 杨茂，陈郁林.基于EMD分解和集对分析的风电功率实时预测[J].电工技术学报，2016,31(21):86-93.YANG Mao,CHEN Yulin.Real-time prediction for wind power based on EMD and set pair analysis[J].Transactions of China Electrotechnical Society,2016,31(21):86-93.
• [6] 文孝强，许洋.基于EMD分解的风力机功率特性分析与预测建模[J/OL].太阳能学报：(2020-12-04)[2021-08-05].http://kns.cnki.net/kcms/detail/11.2082.TK.20201203.1503.002.html.WEN Xiaoqiang,XU Yang.Analysis and prediction modeling of wind power characteristics based on emd decomposition[J/OL].Acta Energiae Solaris Sinica:(2020-12-04)[2021-08-05].http://kns.cnki.net/kcms/detail/11.2082.TK.20201203.1503.002.html.
• [7] 姜贵敏，陈志军，李笑竹，等.基于EEMD-ACS-LSSVM的短期风电功率预测[J].太阳能学报，2020,41(5):77-84.JIANG Guimin,CHEN Zhijun,LI Xiaozhu,et al.Short term wind power prediction based on EEMD-ACS-LSSVM[J].Acta Energiae Solaris Sinica,2020,41(5):77-84.
• [8] 岳晓宇，彭显刚，林俐.鲸鱼优化支持向量机的短期风电功率预测[J].电力系统及其自动化学报，2020,32(2):146-150.YUE Xiaoyu,PENG Xiangang,LIN Li.Short-term wind power forecasting based on whales optimization algorithm and support vector machine[J].Proceedings of the CSU-EPSA,2020,32(2):146-150.
• [9] 孙堃，赵萌萌，沈美娜，等.基于CEEMD和模糊熵的随机森林风力发电功率预测[J].智慧电力，2019,47(10):36-43.SUN Kun,ZHAO Mengmeng,SHEN Meina,et al.Wind Power Forecasting with Random Forest Based on CEEMD and Fuzzy Entropy[J].Smart Power,2019,47(10):36-43.
• [10] TORRES M E,COLOMINAS M A,SCHLOTTHAUER G,et al.A complete ensemble empirical mode decomposition with adaptive noise[C]//IEEE.2011 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).Prague,Czech Republic:IEEE,2011:4144-4147.
• [11] 岳有军，刘英翰，赵辉，等.基于CEEMDAN-SE和DBN的短期电力负荷预测[J].电测与仪表，2020,57(17):59-65.YUE Youjun,LIU Yinghan,Zhao Hui,et al.Short-term load forecasting based on CEEMDAN-SE and DBN[J].Electrical Measurement & Instrumentation,2020,57(17):59-65.
• [12] 赵睿智，丁云飞.基于MEEMD-KELM的短期风电功率预测[J].电测与仪表，2020,57(21):92-98.ZHAO Ruizhi,DING Yunfei.Short-term prediction of wind power based on MEEMD-KELM[J].Electrical Measurement & Instrumentation,2020,57(21):92-98.
• [13] WU S D,WU C W,LIN S G,et al.Analysis of complex time series using refined composite multiscale entropy[J].Physics Letters A,2014,378(20):1369-1374.
• [14] 郑近德，潘海洋，包家汉，等.基于精细复合多尺度熵和自编码的滚动轴承故障诊断方法[J].噪声与振动控制，2019,39(2):175-180.ZHENG Jinde,PAN Haiyang,BAO Jiahan.Fault diagnosis method of rolling bearings based on refined composite multiscale entropy and autoencoder[J].Noise and Vibration Control,2019,39(2):175-180.
• [15] 叶仙，胡洁，田畔，等.基于精细复合多尺度熵与支持向量机的睡眠分期[J].上海交通大学学报，2019,53(3):321-326.YE Xian,HU Jie,TIAN Pan,et al.Automatic sleep scoring based on refined composite multir scale entropy and support vector machine[J].Journal of Shanghai Jiaotong University,2019,53(3):321-326.
• [16] 赵倩，黄景涛.基于EMD-SA-SVR的超短期风电功率预测研究[J].电力系统保护与控制，2020,48(4):89-96.ZHAO Qian,HUANG Jingtao.On ultra-short-term wind power prediction based on EMD-SA-SVR[J].Power System Protection and Control,2020,48(4):89-96.
• [17] 王鹏，陈国初，徐余法，等.改进的EMD及其在风电功率预测中的应用[J].控制工程，2011,18(4):588-591.WANG Peng,CHEN Guochu,XU Yufa,et al.Improved empirical mode decomposition and its application to wind power forecasting[J].Control Engineering of China,2011,18(4):588-591.
• [18] 李永刚，王月，刘丰瑞，等.基于Stacking融合的短期风速预测组合模型[J].电网技术，2020,44(8):2875-2882.LI Yonggang,WANG Yue,LIU Fengrui,et al.Combination model of short-term wind speed prediction based on Stacking Fusion[J].Power System Technology,2020,44(8):2875-2882.
• [19] 董美娜，刘丽平，王泽忠，等.基于Stacking集成学习的有源台区线损率评估方法[J/OL].(2021-01-22)[2021-06-21].http://kns.cnki.net/kcms/detail/23.1202.TH.20210122.1426.010.html.DONG Meina,LIU Liping,WANG Zezhong,et al.A line loss rate evaluation method based on Stacking ensemble learning for transformer district with DG [J/OL].(2021-01-22)[2021-06-21].http://kns.cnki.net/kcms/detail/23.1202.TH.20210122.1426.010.html.
• [20] 刘波，秦川，鞠平，等.基于XGBoost与Stacking模型融合的短期母线负荷预测[J].电力自动化设备，2020,40(3):147-153.LIU Bo,QIN Chuan,JU Ping,et al.Short-term bus load forecasting based on XGBoost and Stacking model fusion[J].Electric Power Automation Equipment,2020,40(3):147-153.
• [21] 卢凯，吴蔚，林观荣，等.基于KNN回归的客运枢纽聚集人数组合预测方法[J/OL].(2020-09-21)[2021-06-19].https://doi.org/10.13229/j.cnki.jdxbgxb20200288.LU Kai,WU Wei,LIN Guanrong,et al.Combination forecasting model for number of assemblingpassengers at transportation terminal based on KNNregression algorithm [J/OL].(2020-09-21)[2021-06-19].https://doi.org/10.13229/j.cnki.jdxbgxb20200288.
• [22] 王阳光，徐民，邓小亮，等.基于小波变换的随机森林模型风力发电预测方法[J].电工技术，2021(8):48-50.WANG Yangguang,XU Min,DENG Xiaoliang,et al.Random forest model of wind power forecasting based on wavelet transform[J].Electric Engineering,2021(8):48-50.
• [23] 李畸勇，张伟斌，赵新哲，等.改进鲸鱼算法优化支持向量回归的光伏最大功率点跟踪[J].电工技术学报，2021,36(9):1771-1781.LI Jiyong,ZHANG Weibin,ZHAO Xinzhe,et al.Global maximum power point tracking for PV array based on support vector regression optimized by improved whale algorithm[J].Transactions of China Electrotechnical Society,2021,36(9):1771-1781.
• [24] 邹旺，江伟，冯俊杰，等.基于ANN和SVM的轴承剩余使用寿命预测[J].组合机床与自动化加工技术，2021(1):32-35.ZOU Wang,JANG Wei,FENG Junjie,et al.Bearing remaining useful life prediction based on artificial neural network and support vector machine[J].Modular Machine Tool & Automatic Manufacturing Technique,2021(1):32-35.
• [25] 赵泽谦，黄强，明波，等.基于多模型随机组合的水文集合预报方法研究[J].水力发电学报，2021,40(1):76-87.ZHAO Zeqian,HUANG Qiang,MING Bo,et al.Hydrological ensemble forecasting method based on stochastic combination of multiple models[J].Journal of Hydroelectric Engineering,2021,40(1):76-87.