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风力发电存在间歇性、随机性、不确定性特征，准确的风电功率预测方法，对于保障电网稳定运行、促进新能源消纳十分重要。基于机器学习的风电功率预测模型，通过对大量数据的特征挖掘及复杂关联，可以建立输入特征与预测功率值之间非线性复杂映射。首先给出了基于机器学习的风电功率预测整体框架，其次归纳了深度学习预测模型、集成学习预测模型、小样本预测模型、在线学习预测模型、物理数据联合预测模型等5种预测模型，并且针对性提出研究建议，进而对5种风力发电功率预测模型进行对比分析，最后简要分析了大模型技术在风电功率预测中的应用，并对基于机器学习小模型及大模型的风力发电功率预测模型进行比较。
Abstract：Wind power generation has intermittent,random,and uncertain characteristics. Accurate wind power prediction methods are crucial for ensuring the stable operation of the power grid and promoting the consumption of new energy. A machine learning based wind power prediction model can establish a nonlinear and complex mapping between input features and predicted power values through feature mining and complex correlations of a large amount of data. Firstly,the overall framework of wind power prediction based on machine learning is given,and then summarizes five prediction models including deep learning prediction model,ensemble learning prediction model,small sample prediction model,online learning prediction model,and physical data joint prediction model. Targeted research suggestions are proposed,and the five wind power prediction models are compared and analyzed.Finally,the application of large model technology in wind power prediction is briefly analyzed,and the wind power prediction models based on machine learning small and large models are compared.
[1]卢毓东，陈益.“双碳”目标下绿色人工智能技术研究综述[J].浙江电力，2023,42(10):45-56.LU Yudong,CHEN Yi.A review of green AI research under carbon peaking and neutrality goals[J].Zhejiang Electric Power,2023,42(10):45-56.
[2]陈海鹏，李赫，阚天洋，等.考虑风电时序特性的深度小波-时序卷积网络超短期风功率预测[J].电网技术，2023,47(4):1653-1662.CHEN Haipeng,LI He,KAN Tianyang,et al.DWT-DTCNA ultrashort-term wind power prediction considering wind power timing characteristics[J].Power System Technology,2023,47(4):1653-1662.
[3]张爱枫，段新宇，何枭峰.基于CNN和LightGBM的新型风电功率预测模型[J].电测与仪表，2021,58(11):121-127.ZHANG Aifeng,DUAN Xinyu,HE Xiaofeng. A new wind power prediction model based on CNN and LightGBM[J]. Electrical Measurement&Instrumentation,2021,58(11):121-127.
[4]张淑清，杜灵韵，王册浩，等.基于格拉姆角场与改进CNNResNet的风电功率预测方法[J].电网技术，2023,47(4):1540-1547.ZHANG Shuqing,DU Lingyun,WANG Cehao,et al. Wind power forecasting method based on GAF and improved CNN-ResNet[J].Power System Technology,2023,47(4):1540-1547.
[5]张淑清，杨振宁，姜安琦，等.基于EN-SKPCA降维和FPA优化LSTMNN的短期风电功率预测[J].太阳能学报，2022,43(6):204-211.ZHANG Shuqing,YANG Zhenning,JIANG Anqi,et al.Short term wind power prediction based on en-skpca dimensionality reduction and fpa optimizing lstmnn[J].Acta Energiae Solaris Sinica,2022,43(6):204-211.
[6]肖白，张博，王辛玮，等.基于组合模态分解和深度学习的短期风电功率区间预测[J].电力系统自动化，2023,47(17):110-117.XIAO Bai,ZHANG Bo,WANG Xinwei,et al. Short-term wind power interval prediction based on combined mode decomposition and deep learning[J].Automation of Electric Power Systems,2023,47(17):110-117.
[7]刘新宇，蒲欣雨，李继方，等.基于贝叶斯优化的VMD-GRU短期风电功率预测[J].电力系统保护与控制，2023,51(21):158-165.LIU Xinyu,PU Xinyu,LI Jifang,et al. Short-term wind power prediction of a VMD-GRU based on Bayesian optimization[J].Power System Protection and Control,2023,51(21):158-165.
[8]李青，张新燕，马天娇，等.基于SSA-CNN-BiGRU-Attention的超短期风电功率预测模型[J].电机与控制应用，2023,50(5):61-71.LI Qing,ZHANG Xinyan,MA Tianjiao,et al. Ultra-short term forecasting model of wind power based on SSA-CNN-BiGRUattention[J]. Electric Machines&Control Application,2023,50(5):61-71.
[9]郭恒宽，田建艳，刘竖威，等.基于改进Sequence2Sequence架构的LSTM超短期可解释风电功率预测[J/OL].控制工程：1-12[2024-06-19].https：//doi.org/10.14107/j.cnki.kzgc.20230921.GUO Hengkuan,TIAN Jianyan,LIU Liwei,et al.LSTM ultra short term interpretable wind power prediction based on improved Sequence2Sequence architecture[J/OL].Control Engineering:1-12[2024-06-19]https：//doi.org/10.14107/j.cnki.kzgc.20230921.
[10]晋孟雪.基于改进VMD和深度学习的风电功率预测研究[D].西安：西安理工大学，2023.
[11]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.
[12]李练兵，高国强，吴伟强，等.考虑特征重组与改进Transformer的风电功率短期日前预测方法[J].电网技术，2024,48(4):1466-1480.LI Lianbing,GAO Guoqiang,WU Weiqiang,et al.Short-term dayahead wind power prediction considering feature recombination and improved transformer[J].Power System Technology,2024,48(4):1466-1480.
[13]李士哲，王霄慧，刘帅.考虑多变量相关性改进的风电场Transformer中长期预测模型[J].智慧电力，2024,52(4):62-68,107.LI Shizhe,WANG Xiaohui,LIU Shuai. Improved transformer medium and long term prediction model of wind farm considering multivariate correlation[J].Smart Power,2024,52(4):62-68,107.
[14]李国栋，徐明扬.基于KCR-Informer的长期风电功率预测研究[J].电力信息与通信技术，2024,22(4):55-62.LI Guodong,XU Mingyang. Research on long-term wind power prediction based on KCR-informer[J].Electric Power Information and Communication Technology,2024,22(4):55-62.
[15]陈万志，戎馨鑫，王天元.改进Informer网络的风电功率短期预测[J].计算机系统应用，2024,33(5):118-126.CHEN Wanzhi,RONG Xinxin,WANG Tianyuan.Short-term wind power prediction based on improved informer network[J].Computer Systems and Applications,2024,33(5):118-126.
[16]苏向敬，聂良钊，李超杰，等.基于MSTAGNN模型的可解释海上风电场多风机出力预测[J].电力系统自动化，2023,47(9):88-98.SU Xiangjing,NIE Liangzhao,LI Chaojie,et al.Interpretable power output prediction of multiple wind turbines for offshore wind farm based on multiple spatio-temporal attention graph neural network model[J]. Automation of Electric Power Systems,2023,47(9):88-98.
[17]黄玲玲，石孝华，符杨，等.基于DCGCN模型的海上风电场超短期功率预测[J].电力系统自动化，2024,48(15):64-72.HUANG Lingling,SHI Xiaohua,FU Yang,et al.Ultra-short-term power prediction for offshore wind farms based on dual channel graph convolution network model[J].Automation of Electric Power Systems,2024,48(15):64-72.
[18]陈冲，陈杰，张慧，等.深度学习可解释性综述[J].计算机科学，2023,50(5):52-63.CHEN Chong,CHEN Jie,ZHANG Hui,et al. Review on interpretability of deep learning[J]. Computer Science,2023,50(5):52-63.
[19]崔杨，王议坚，黄彦浩，等.基于多元注意力框架与引导式监督学习的闭环风电功率超短期预测策略[J].中国电机工程学报，2023,43(4):1334-1346.CUI Yang,WANG Yijian,HUANG Yanhao,et al. Closed-loop wind power ultra-short-term forecasting strategy based on multiattention framework and guided supervised learning[J].Proceedings of the CSEE,2023,43(4):1334-1346.
[20]王永生，李海龙，关世杰，等.基于变换域分析和XGBoost算法的超短期风电功率预测模型[J/OL].高电压技术：1-12[2024-06-19].https：//doi.org/10.13336/j.1003-6520.hve.20231942.WANG Yongsheng,LI Hailong,GUAN Shijie,et al.A ultra short term wind power prediction model based on transform domain analysis and XGBoost algorithm[J/OL].High Voltage Technology:1-12[2024-06-19]. https：//doi. org/10.13336/j. 1003-6520.hve.20231942.
[21]叶家豪，魏霞，黄德启，等.基于灰色关联分析的BSO-ELMAdaBoost风电功率短期预测[J].太阳能学报，2022,43(3):426-432.YE Jiahao,WEI Xia,HUANG Deqi,et al. Short-term forecast of wind power based on bso-elm-adaboost with grey correlation analysis[J].Acta Energiae Solaris Sinica,2022,43(3):426-432.
[22]高金兰，李豪，邓蒙.基于GAVMD-SGRU模型的风电场短期功率预测[J].吉林大学学报：信息科学版，2021,39(6):647-655.GAO Jinlan,LI Hao,DENG Meng.Short term power prediction of wind farm based on GAVMD-SGRU model[J]. Journal of Jilin University:Information Science Edition,2021,39(6):647-655.
[23]石立贤，金怀平，杨彪，等.基于局部学习和多目标优化的选择性异质集成超短期风电功率预测方法[J].电网技术，2022,46(2):568-577.SHI Lixian, JIN Huaiping, YANG Biao, et al. Selective heterogeneous ensemble for ultra-short-term wind power forecasting based on local learning and multi-objective optimization[J].Power System Technology,2022,46(2):568-577.
[24]鲁泓壮，丁云飞，汪鹏宇.基于信息融合和堆叠模型的超短期风电功率预测[J].可再生能源，2022,40(3):344-349.LU Hongzhuang,DING Yunfei,WANG Pengyu.Ultra-short-term wind power forecasting based on information fusion and stacking model[J].Renewable Energy Resources,2022,40(3):344-349.
[25]朱梓彬，孟安波，欧祖宏，等.基于多元模态分解与多目标算法优化的深度集成学习模型的超短期风电功率预测[J].现代电力，2024,41(3):458-469.ZHU Zibin,MENG Anbo,OU Zuhong,et al.Ultra-short-term wind power prediction based on deep ensemble learning model using multivariate mode decomposition and multi-objective optimization[J].Modern Electric Power,2024,41(3):458-469.
[26]刘兴，王艳，纪志成.基于随机森林的风电功率短期预测方法[J].系统仿真学报，2021,33(11):2606-2614.LIU Xing,WANG Yan,JI Zhicheng. Short-term wind power prediction method based on random forest[J]. Journal of System Simulation,2021,33(11):2606-2614.
[27]潘霄，张明理，刘德宝，等.基于鲁棒多标签生成对抗的风电场日前出力区间预测[J].电力系统自动化，2022,46(10):216-223.PAN Xiao,ZHANG Mingli,LIU Debao,et al.Interval prediction of wind farm day-ahead output based on robust multi-label generative adversarial[J].Automation of Electric Power Systems,2022,46(10):216-223.
[28]叶林，李奕霖，裴铭，等.寒潮天气小样本条件下的短期风电功率组合预测[J].中国电机工程学报，2023,43(2):543-554.YE Lin,LI Yilin,PEI Ming,et al.Combined approach for shortterm wind power forecasting under cold weather with small sample[J].Proceedings of the CSEE,2023,43(2):543-554.
[29]周军，王渴心，王岩.融合迁移学习与CGAN的风电集群功率超短期预测[J].电力系统及其自动化学报，2024,36(5):9-18.ZHOU Jun,WANG Kexin,WANG Yan. Ultra-short-term power forecasting of wind power cluster based on migration learning and CGAN[J].Proceedings of the CSU-EPSA,2024,36(5):9-18.
[30]宋技峰，彭小圣，杨子民，等.基于偏差补偿TCN-LSTM和梯级迁移策略的短期风电功率预测[J].南方电网技术，2023,17(12):71-79.SONG Jifeng,PENG Xiaosheng,YANG Zimin,et al. Short-term wind power prediction based on deviation compensation TCNLSTM and step transfer strategy[J]. Southern Power System Technology,2023,17(12):71-79.
[31]欧祖宏.基于模型迁移与数据增强的少样本风电预测方法研究[D].广州：广东工业大学，2022.
[32]苏鹏程.面向风电时序数据的迁移学习算法研究与应用[D].南京：东南大学，2019.
[33]杨茂，周宜.计及风电场状态的风电功率超短期预测[J].中国电机工程学报，2019,39(5):1259-1267.YANG Mao,ZHOU Yi.Ultra-short-term prediction of wind power considering wind farm status[J].Proceedings of the CSEE,2019,39(5):1259-1267.
[34]符杨，任子旭，魏书荣，等.基于改进LSTM-TCN模型的海上风电超短期功率预测[J].中国电机工程学报，2022,42(12):4292-4302.FU Yang,REN Zixu,WEI Shurong,et al.Ultra-short-term power prediction of offshore wind power based on improved LSTM-TCN model[J].Proceedings of the CSEE,2022,42(12):4292-4302.
[35]王耀健，顾洁，温洪林，等.基于在线高斯过程回归的短期风电功率概率预测[J].电力系统自动化，2024,48(11):197-207.WANG Yaojian,GU Jie,WEN Honglin,et al. Probability prediction of short-term wind power based on online Gaussian process regression[J]. Automation of Electric Power Systems,2024,48(11):197-207.
[36]潘春阳，文书礼，朱淼，等.基于概念漂移监测与增量更新机制的超短期风电功率在线预测[J/OL].中国电机工程学报，2023:1-12(2023-12-27)[2024-06-21].https：//kns.cnki.net/kcms/detail/11.2107.TM.20231227.0928.002.html.PAN Chunyang,WEN Shuli,ZHU Miao,et al.Online ultra-shortterm wind power forecasting based on concept drift detection and incremental updating mechanism[J/OL].Proceedings of the CSEE,2023:1-12(2023-12-27)[2024-06-21].https：//kns.cnki.net/kcms/detail/11.2107.TM.20231227.0928.002.html.
[37]李文斌，熊亚锟，范祉辰，等.持续学习的研究进展与趋势[J].计算机研究与发展，2024,61(6):1476-1496.LI Wenbin,XIONG Yakun,FAN Zhichen,et al. Advances and trends of continual learning[J].Journal of Computer Research and Development,2024,61(6):1476-1496.
[38]邬永，王冰，陈玉全，等.融合精细化气象因素与物理约束的深度学习模型在短期风电功率预测中的应用[J].电网技术，2024,48(4):1455-1465.WU Yong,WANG Bing,CHEN Yuquan,et al.Application of deep learning model integrating refined meteorological factors and physical constraints in short-term wind power forecasting[J].Power System Technology,2024,48(4):1455-1465.
[39]杨茂，王达，王小海，等.基于数据物理混合驱动的超短期风电功率预测模型[J/OL].高电压技术：1-11[2024-06-19].https：//doi.org/10.13336/j.1003-6520.hve.20230401.YANG Mao,WANG Da,WANG Xiaohai,et al.A ultra short term wind power prediction model based on data physics hybrid drive[J/OL].High Voltage Technology:1-11[2024-06-19].https：//doi.org/10.13336/j.1003-6520.hve.20230401.
[40]蒲天骄，韩笑.新型电力系统中人工智能应用的关键技术[J].电力信息与通信技术，2024,22(1):1-13.PU Tianjiao,HAN Xiao. Research on key technologies in the application of artificial intelligence in new type power systems[J].Electric Power Information and Communication Technology,2024,22(1):1-13.
[41]尚海勇，刘利强，齐咏生，等.基于数字孪生技术的风电机组建模研究[J].太阳能学报，2023,44(5):391-400.SHANG Haiyong,LIU Liqiang,QI Yongsheng,et al.Research on wind turbine modeling based on digital twin technology[J]. Acta Energiae Solaris Sinica,2023,44(5):391-400.
[42]黄小猛，林岩銮，熊巍，等.数值预报AI气象大模型国际发展动态研究[J].大气科学学报，2024,47(1):46-54.HUANG Xiaomeng,LIN Yanluan,XIONG Wei,et al.Research on international development trends of numerical forecast AI meteorological model[J]. Transactions of Atmospheric Sciences,2024,47(1):46-54.
[43]李鹏，余涛，李立浧，等.电力人工智能的演变与展望——从专业智能走向通用智能[J/OL].电力系统自动化，2024:1-21(2024-06-24)[2024-07-15].https：//kns.cnki.net/kcms/detail/32.1180.TP.20240621.1336.002.html.LI Peng,YU Tao,LI Licheng,et al. Retrospect and prospect of artificial intelligence for electric power system——from domain intelligence to general intelligence[J/OL].Automation of Electric Power Systems,2024:1-21(2024-06-24)[2024-07-15].https：//kns.cnki.net/kcms/detail/32.1180.TP.20240621.1336.002.html.
基本信息:
DOI：10.20097/j.cnki.issn1007-9904.2025.08.005
中图分类号:TM614;TP181
引用信息:
[1]李特,黄孜滢.机器学习在风力发电功率预测中应用综述[J].山东电力技术,2025,52(08):45-55.DOI:10.20097/j.cnki.issn1007-9904.2025.08.005.