风功率预测关键技术及其研究应用综述

王磊; 滕伟; 武鑫; 高青风; 柳亦兵

doi:10.19805/j.cnki.jcspe.2026.250174

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风功率预测关键技术及其研究应用综述

新能源与储能 | 更新时间：2026-10-10

- 风功率预测关键技术及其研究应用综述
- A Review of Key Technologies and Research Applications in Wind Power Prediction
- 动力工程学报 2026年46卷第2期页码：98-112
- 作者机构：
  
  华北电力大学电站能量传递转化与系统教育部重点实验室,北京,102206
- 作者简介：
  
  王磊(1986—),男,河南确山人,博士,研究方向为风电机组故障,E-mail:hais1998@163.com
- 基金信息：
  
  国家自然科学基金资助项目(51775186)
- DOI：10.19805/j.cnki.jcspe.2026.250174
  中图分类号：
- 网络首发：2026-02-10，
  
  纸质出版：2026-02-10
- 稿件说明：
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王磊,滕伟,武鑫,高青风,柳亦兵. 风功率预测关键技术及其研究应用综述动力工程学报, 2026, 46(2): 98-112 https://doi.

org/10.19805/j.cnki.jcspe.2026.250174
王磊,滕伟,武鑫,高青风,柳亦兵. 风功率预测关键技术及其研究应用综述动力工程学报, 2026, 46(2): 98-112 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.250174.

org/10.19805/j.cnki.jcspe.2026.250174 DOI：

摘要

风电有显著的间歇性与随机性特征

其输出功率随风速变化剧烈波动

特别是在电网疏散、渗透和并网方面

给电力系统的管理带来许多挑战。风功率预测技术作为缓解风电不确定性的核心手段

对提升电网稳定性、降低弃风率、优化电力市场交易及推动风电可持续发展具有重要意义。系统地解析了风功率预测的类型划分、基本原理架构及主流方法

深入对比物理建模法、统计分析法、机器学习法、组合法等风功率预测方法的适用场景、优势、局限及评价指标体系。在此基础上

全面综述预测精度提升的关键技术路径

涵盖多源数据融合、深度学习算法优化、误差校正机制等前沿研究方向

并总结最新研究成果。最后展望了风功率预测技术的未来发展趋势

提出基于数字孪生、强化学习与气象耦合建模的创新解决方案。

Abstract

Wind power exhibits significant intermittency and stochastic characteristics

with its output power experiencing severe fluctuations due to wind speed variations. This poses substantial challenges for power system management

particularly in grid dispersion

penetration

and grid connection. As a core means to mitigate wind power uncertainty

wind power prediction technology plays a crucial role in enhancing grid stability

reducing wind curtailment rates

optimizing electricity market transactions

and promoting the sustainable development of wind power. This study systematically analyzed the classification

fundamental principles

and mainstream methodologies of wind power prediction

conducting an in-depth comparisons of the application scenarios

advantages

limitations

and evaluation index systems of various wind power prediction methods

such as physical modeling

statistical analysis

machine learning

and hybrid methods. Based on this

a comprehensively review was presented about the key technological pathways for improving prediction accuracy

covering cutting-edge research directions such as multi-source data fusion

deep learning algorithm optimization

and error correction mechanisms

and the latest research findings were also summarized. Finally

the prospect of the future development trends in wind power prediction technology was provided

proposing innovative solutions based on digital twins

reinforcement learning

and meteorological coupled modeling.

关键词

Keywords

references

武鑫, 冯歌, 熊星宇. 用于风功率平抑的SOEC系统功率控制策略[J]. 动力工程学报, 2023, 43(12):1626-1633, 1674. WU Xin, FENG Ge, XIONG Xingyu. Power control strategy of the SOEC system for smoothing wind power fluctuations[J]. Journal of Chinese Society of Power Engineering, 2023, 43(12):1626-1633, 1674.

EL RAFEI M, SHERWOOD S, EVANS J P, et al. Analysis of extreme wind gusts using a high-resolution Australian regional reanalysis[J]. Weather and Climate Extremes, 2023, 39:100537.

王勃. 风力发电功率预测技术及应用[M]. 北京:中国电力出版社, 2019.

国家能源局. 风电功率预测技术规定:NB/T 10205-2019[S]. 北京:中国电力出版社, 2019.

国家能源局. 风电功率预测系统功能规范:NB/T 31046-2022[S]. 北京:中国电力出版社, 2022.

YU Guangzheng, LIU Chengquan, TANG Bo, et al. Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution[J]. Renewable Energy, 2022, 199:599-612.

SHAO Lei, HUANG Wenxuan, LIU Hongli, et al. Study of wind power prediction in ELM based on improved SSA[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2025, 20(6):853-861.

曾娜梅, 霍志红, 许昌, 等. 基于改进HHT的分钟级超短期风速预测[J]. 动力工程学报, 2021, 41(4):309-315, 329. ZENG Namei, HUO Zhihong, XU Chang, et al. Minute-scale ultra-short-term wind speed prediction bas-ed on improved HHT[J]. Journal of Chinese Society of Power Engineering, 2021, 41(4):309-315, 329.

ZHANG Fei, LI Pengcheng, GAO Lu, et al. Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting[J]. Renewable Energy, 2021, 169:129-143.

WANG Jianzhou, NIU Tong, LU Haiyan, et al. A novel framework of reservoir computing for deterministic and probabilistic wind power forecasting[J]. IEEE Transactions on Sustainable Energy, 2020, 11(1):337-349.

SUN Mucun, FENG Cong, ZHANG Jie. Multi-distribution ensemble probabilistic wind power forecasting[J]. Renewable Energy, 2020, 148:135-149.

CHEN Yinsong, YU S S, LIM C P, et al. Multi-objective estimation of optimal prediction intervals for wind power forecasting[J]. IEEE Transactions on Sustainable Energy, 2024, 15(2):974-985.

SHARMA N, BHAKAR R, JAIN P. Optimal reconciliation of hierarchical wind power forecasts of correlated wind farms[J]. Sustainable Energy, Grids and Networks, 2023, 35:101091.

OZKAN M B, KARAGOZ P. Reducing the cost of wind resource assessment:using a regional wind power forecasting method for assessment[J]. International Journal of Energy Research, 2021, 45(9):13182-13197.

魏乐, 戴泽, 陈远野, 等. 考虑多对一时空特征的短期风功率组合预测模型[J]. 动力工程学报, 2024, 44(12):1869-1877. WEI Le, DAI Ze, CHEN Yuanye, et al. Combined prediction model of short-term wind power by considering many-to-one spatial-temporal features[J]. Journal of Chinese Society of Power Engineering, 2024, 44(12):1869-1877

OUYANG Tinghui, ZHA Xiaoming, QIN Liang, et al. Prediction of wind power ramp events based on residual correction[J]. Renewable Energy, 2019, 136:781-792.

LIU Lei, LIU Jicheng, YE Yu, et al. Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty[J]. Renewable Energy, 2023, 205:598-607.

LIU Zhuoyi, HARA R, KITA H. Hybrid forecasting system based on data area division and deep learning neural network for short-term wind speed forecasting[J]. Energy Conversion and Management, 2021, 238:114136.

CHEN Gonggui, TANG Bangrui, ZENG Xianjun, et al. Short-term wind speed forecasting based on long short-term memory and improved BP neural network[J]. International Journal of Electrical Power & Energy Systems, 2022, 134:107365.

李莉, 刘永前, 杨勇平, 等. 基于CFD流场预计算的短期风速预测方法[J]. 中国电机工程学报, 2013, 33(7):27-32. LI Li, LIU Yongqian, YANG Yongping, et al. Short-term wind speed forecasting based on CFD pre-calculated flow fields[J]. Proceedings of the CSEE, 2013, 33(7):27-32.

OUYANG Tinghui, HUANG Heming, HE Yusen. Ramp events forecasting based on long-term wind power prediction and correction[J]. IET Renewable Power Generation, 2019, 13(15):2793-2801.

GU Jiu, WANG Yining, XIE Da, et al. Wind farm NWP data preprocessing method based on t-SNE[J]. Energies, 2019, 12(19):3622.

牛东晓, 纪会争. 风电功率物理预测模型引入误差量化分析方法[J]. 电力系统自动化, 2020, 44(8):57-65. NIU Dongxiao, JI Huizheng. Quantitative analysis method for errors introduced by physical prediction model of wind power[J]. Automation of Electric Power Systems, 2020, 44(8):57-65.

朱霄珣, 韩中合. 基于PSO参数优化的LS-SVM风速预测方法研究[J]. 中国电机工程学报, 2016, 36(23):6337-6342, 6598. ZHU Xiaoxun, HAN Zhonghe. Research on LS-SVM wind speed prediction method based on PSO[J]. Proceedings of the CSEE, 2016, 36(23):6337-6342, 6598.

冯双磊, 王伟胜, 刘纯, 等. 风电场功率预测物理方法研究[J]. 中国电机工程学报, 2010, 30(2):1-6. FENG Shuanglei, WANG Weisheng, LIU Chun, et al. Study on the physical approach to wind power prediction[J]. Proceedings of the CSEE, 2010, 30(2):1-6.

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 Rongfu, ZHANG Tao, HE Qing, et al. Review on key technologies and applications in wind power forecasting[J]. High Voltage Engineering, 2021, 47(4):1129-1143.

高志伟, 王永平, 杨根铨. 低纬度高海拔复杂地形风电功率预测预报技术[M]. 北京:气象出版社, 2018.

李丽, 叶林. 基于改进持续法的短期风电功率预测[J]. 农业工程学报, 2010, 26(12):182-187. LI Li, YE Lin. Short-term wind power forecasting based on an improved persistence approach[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(12):182-187.

YU Ruiguo, SUN Yingzhou, LI Xuewei, et al. Time series cross-correlation network for wind power prediction[J]. Applied Intelligence, 2023, 53(10):11403-11419.

杨茂, 张书天, 王勃, 等. 基于混合分位数回归长短期记忆神经网络的风电功率短期区间预测[J]. 太阳能学报, 2025, 46(2):582-590. YANG Mao, ZHANG Shutian, WANG Bo, et al. Short-term wind power interval prediction based on mixed quantile regression long and short-term memory neural network[J]. Acta Energiae Solaris Sinica, 2025, 46(2):582-590.

柳天虹, 齐胜利, 裔扬, 等. 基于分位数回归的改进权重GRU风电功率区间预测[J]. 太阳能学报, 2025, 45(12):291-298. LIU Tianhong, QI Shengli, YI Yang, et al. Improved weighted GRU wind power interval prediction based on quantile regression[J]. Acta Energiae Solaris Sinica, 2025, 45(12):291-298.

HUANG Yu, LI Xuxin, LI Dui, et al. Probabilistic prediction of wind farm power generation using non-crossing quantile regression[J]. Control Engineering Practice, 2025, 156:106226.

YU Yixiao, HAN Xueshan, YANG Ming, et al. Probabilistic prediction of regional wind power based on spatiotemporal quantile regression[J]. IEEE Transactions on Industry Applications, 2020, 56(6):6117-6127.

WANG Yun, ZOU Runmin, LIU Fang, et al. A review of wind speed and wind power forecasting with deep neural networks[J]. Applied Energy, 2021, 304:117766.

WANG Sen, SUN Yonghui, ZHANG Wenjie, et al. Optimization of deterministic and probabilistic forecasting for wind power based on ensemble learning[J]. Energy, 2025, 319:134884.

NAYAK A K, SHARMA K C, BHAKAR R, et al. Probabilistic online learning framework for short-term wind power forecasting using ensemble bagging regression model[J]. Energy Conversion and Management, 2025, 323:119142.

LI Yanting, WU Zhenyu, WANG Peng, et al. A farm-level wind power probabilistic forecasting method based on wind turbines clustering and heteroscedastic model[J]. Journal of Renewable and Sustainable Energy, 2024, 16(4):043309.

CHEN Yuejiang, HE Yingjing, XIAO Jiangwen, et al. Hybrid model based on similar power extraction and improved temporal convolutional network for probabilistic wind power forecasting[J]. Energy, 2024, 304:131966.

杨楠, 黄禹, 叶迪, 等. 基于NACEMD和改进非参数核密度估计的风功率波动性概率分布研究[J]. 电网技术, 2019, 43(3):910-917. YANG Nan, HUANG Yu, YE Di, et al. Study on probability distribution of wind power fluctuation based on NACEMD and improved nonparametric kernel density estimation[J]. Power System Technology, 2019, 43(3):910-917.

ZHANG Wanying, HE Yaoyao, YANG Shanlin. A multi-step probability density prediction model based on Gaussian approximation of quantiles for offshore wind power[J]. Renewable Energy, 2023, 202:992-1011.

LI Jianfang, JIA Li, ZHOU Chengyu. Probability density function based adaptive ensemble learning with global convergence for wind power prediction[J]. Energy, 2024, 312:133573.

TAN Ling, CHEN Yihe, XIA Jingming, et al. Research on the short-term wind power prediction with dual branch multi-source fusion strategy[J]. Energy, 2024, 291:130402.

CUI Wenkang, WAN Can, SONG Yonghua. Ensemble deep learning-based non-crossing quantile regression for nonparametric probabilistic forecasting of wind power generation[J]. IEEE Transactions on Power Systems, 2023, 38(4):3163-3178.

CHEN Yuejiang, XIAO Jiangwen, WANG Yanwu, et al. Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation[J]. Applied Energy, 2025, 377:124356.

ARRIETA-PRIETO M, SCHELL K R. Spatially transferable machine learning wind power prediction models:v? logit random forests[J]. Renewable Energy, 2024, 223:120066.

KONG Lingxing, LIU Kailong, FU Deyi, et al. Wind power regression prediction based on stacked LSTMs with attention mechanisms for evaluating technological improvement effects of wind turbines[J]. Journal of Intelligent & Fuzzy Systems, 2023, 45(1):51-62.

PARRI S, TEEPARTHI K, KOSANA V. A hybrid VMD based contextual feature representation approach for wind speed forecasting[J]. Renewable Energy, 2023, 219:119391.

LAHOUAR A, BEN HADJ SLAMA J. Hour-ahead wind power forecast based on random forests[J]. Renewable Energy, 2017, 109:529-541.

林涛, 董栅, 秦冬阳, 等. 基于支持向量回归的风电场短期功率预测[J]. 中南民族大学学报(自然科学版), 2017, 36(4):95-99. LIN Tao, DONG Shan, QIN Dongyang, et al. Short-term forecast of wind farm power based on support vector regression model[J]. Journal of South-Central University for Nationalities (Natural Science Edition), 2017, 36(4):95-99.

HANIFI S, CAMMARONO A, ZARE-BEHTASH H. Advanced hyperparameter optimization of deep learning models for wind power prediction[J]. Renewable Energy, 2024, 221:119700.

ZHANG Chu, MA Huixin, HUA Lei, et al. An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction[J]. Energy, 2022, 254:124250.

ZHANG Yagang, PAN Zhiya, WANG Hui, et al. Achieving wind power and photovoltaic power prediction:an intelligent prediction system based on a deep learning approach[J]. Energy, 2023, 283:129005.

ADOMAKO A B, JAMSHIDI E J, YUSUP Y, et al. Deep learning approaches for bias correction in WRF model outputs for enhanced solar and wind energy estimation:a case study in east and west Malaysia[J]. Ecological Informatics, 2024, 84:102898.

BACKHUS J, RAO A R, VENKATRAMAN C, et al. Equipment health assessment:time series analysis for wind turbine performance[J]. Applied Sciences, 2024, 14(8):3270.

SHAO Haijian, DENG Xing, JIANG Yingtao. A novel deep learning approach for short-term wind power forecasting based on infinite feature selection and recurrent neural network[J]. Journal of Renewable and Sustainable Energy, 2018, 10(4):043303.

XIANG Ling, FU Xiaomengting, YAO Qingtao, et al. A novel model for ultra-short term wind power prediction based on vision transformer[J]. Energy, 2024, 294:130854.

YAN Leiming, WU Siqi, LI Shaopeng, et al. SEAformer:frequency domain decomposition transformer with signal enhanced for long-term wind power forecasting[J]. Neural Computing and Applications, 2024, 36(33):20883-20906.

GOH H H, DING Chunyang, DAI Wei, et al. A hybrid short-term wind power forecasting model considering significant data loss[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2024, 19(3):349-361.

MENG Anbo, ZHANG Haitao, YIN Hao, et al. A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN[J]. Energy, 2023, 283:129139.

LIU Fang, LIU Qianyi, TAO Qing, et al. Deep reinforcement learning based energy storage management strategy considering prediction intervals of wind power[J]. International Journal of Electrical Power & Energy Systems, 2023, 145:108608.

ZHAO Jing, GUO Yiyi, LIN Yihua, et al. A novel dynamic ensemble of numerical weather prediction for multi-step wind speed forecasting with deep reinforcement learning and error sequence modeling[J]. Energy, 2024, 302:131787.

WANG Lin, TAO Rui, HU Huanling, et al. Effective wind power prediction using novel deep learning network:stacked independently recurrent autoencoder[J]. Renewable Energy, 2021, 164:642-655.

ZHENG Zhong, YANG Luoxiao, ZHANG Zijun. Conditional variational autoencoder informed probabilistic wind power curve modeling[J]. IEEE Transactions on Sustainable Energy, 2023, 14(4):2445-2460.

CHAKA M D, SEMIE A G, MEKONNEN Y S, et al. Improving wind speed forecasting at Adama wind farm II in Ethiopia through deep learning algorithms[J]. Case Studies in Chemical and Environmental Engineering, 2024, 9:100594.

WANG Shuangxin, SHI Jiarong, YANG Wei, et al. High and low frequency wind power prediction based on Transformer and BiGRU-Attention[J]. Energy, 2024, 288:129753.

SASSER C, YU Meilin, DELGADO R. Improvement of wind power prediction from meteorological characterization with machine learning models[J]. Renewable Energy, 2022, 183:491-501.

LV Jiaqing, ZHENG Xiaodong, PAWLAK M, et al. Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms[J]. Renewable Energy, 2021, 177:181-192.

TASCIKARAOGLU A, UZUNOGLU M. A review of combined approaches for prediction of short-term wind speed and power[J]. Renewable and Sustainable Energy Reviews, 2014, 34:243-254.

李军, 贾志强. 基于模型组合的短期风功率预测研究[J]. 自动化应用, 2021(9):1-3. LI Jun, JIA Zhiqiang. Research on short-term wind power prediction based on model combination[J]. Automation Application, 2021(9):1-3

LI Menglin, YANG Ming, YU Yixiao, et al. Adaptive weighted combination approach for wind power forecast based on deep deterministic policy gradient method[J]. IEEE Transactions on Power Systems, 2024, 39(2):3075-3087.

张学清, 梁军, 张熙, 等. 基于样本熵和极端学习机的超短期风电功率组合预测模型[J]. 中国电机工程学报, 2013, 33(25):33-40. ZHANG Xueqing, LIANG Jun, ZHANG Xi, et al. Combined model for ultra short-term wind power prediction based on sample entropy and extreme learning machine[J]. Proceedings of the CSEE, 2013, 33(25):33-40.

AZIMI R, GHOFRANI M, GHAYEKHLOO M. A hybrid wind power forecasting model based on data mining and wavelets analysis[J]. Energy Conversion and Management, 2016, 127:208-225.

JIANG Li, WANG Yifan. A wind power forecasting model based on data decomposition and cross-attention mechanism with cosine similarity[J]. Electric Power Systems Research, 2024, 229:110156.

ZHEN Zhao, QIU Gang, MEI Shengwei, et al. An ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling[J]. International Journal of Electrical Power & Energy Systems, 2022, 135:107502.

付炳喆, 王玮, 任国瑞, 等. 考虑增强特征选择的深度卷积-时序网络短期风功率预测[J]. 动力工程学报, 2024, 44(10):1565-1573. FU Bingzhe, WANG Wei, REN Guorui, et al. Short-term wind power prediction with deep convolutional-temporal networks considering enhanced feature selection[J]. Journal of Chinese Society of Power Engineering, 2024, 44(10):1565-1573.

薛阳, 王琳, 王舒, 等. 一种结合CNN和GRU网络的超短期风电预测模型[J]. 可再生能源, 2019, 37(3):456-462. XUE Yang, WANG Lin, WANG Shu, et al. An ultra-short-term wind power forecasting model combined with CNN and GRU networks[J]. Renewable Energy Resources, 2019, 37(3):456-462.

宋江涛, 崔双喜, 樊小朝, 等. 基于SGMD-SE与优化TCN-BiLSTM/BiGRU的超短期风功率预测[J]. 太阳能学报, 2024, 45(10):588-596. SONG Jiangtao, CUI Shuangxi, FAN Xiaochao, et al. Ultra-short-term wind power prediction based on SGMD-SE and optimized TCN-BiLSTM/BiGRU[J]. Acta Energiae Solaris Sinica, 2024, 45(10):588-596.

韩宇超, 同向前, 邓亚平. 基于概率密度估计与时序Transformer网络的风功率日前区间预测[J]. 中国电机工程学报, 2024, 44(23):9285-9295. HAN Yuchao, TONG Xiangqian, DENG Yaping. Probabilistic distribution estimation and temporal transformer-based interval prediction in day-ahead wind power prediction[J]. Proceedings of the CSEE, 2024, 44(23):9285-9295.

丁华杰, 宋永华, 胡泽春, 等. 基于风电场功率特性的日前风电预测误差概率分布研究[J]. 中国电机工程学报, 2013, 33(34):136-144. DING Huajie, SONG Yonghua, HU Zechun, et al. Probability density function of day-ahead wind power forecast errors based on power curves of wind farms[J]. Proceedings of the CSEE, 2013, 33(34):136-144.

张晓英, 张晓敏, 廖顺, 等. 基于聚类与非参数核密度估计的风电功率预测误差分析[J]. 太阳能学报, 2019, 40(12):3594-3604. ZHANG Xiaoying, ZHANG Xiaomin, LIAO Shun, et al. Prediction error analysis of wind power based on clustering and non-parametric kernel density estimation[J]. Acta Energiae Solaris Sinica, 2019, 40(12):3594-3604.

唐新姿, 顾能伟, 黄轩晴, 等. 风电功率短期预测技术研究进展[J]. 机械工程学报, 2022, 58(12):213-236. TANG Xinzi, GU Nengwei, HUANG Xuanqing, et al. Progress on short term wind power forecasting technology[J]. Journal of Mechanical Engineering, 2022, 58(12):213-236.

梅勇, 李霄, 胡在春, 等. 基于风电机组控制原理的风功率数据识别与清洗方法[J]. 动力工程学报, 2021, 41(4):316-322, 329. MEI Yong, LI Xiao, HU Zaichun, et al. Identification and cleaning of wind power data methods based on control principle of wind turbine generator system[J]. Journal of Chinese Society of Power Engineering, 2021, 41(4):316-322, 329.

孙朋杰, 王彬滨, 陈正洪, 等. 测风塔风速插补对风功率密度误差的影响分析[J]. 气象科技进展, 2019, 9(2):62-65. SUN Pengjie, WANG Binbin, CHEN Zhenghong, et al. Analysis of the influence of wind speed interpolation on wind power density error from wind tower data[J]. Advances in Meteorological Science and Technology, 2019, 9(2):62-65.

BUHAN S, ZKAZAN Y, ADIRCI I. Wind pattern recognition and reference wind mast data correlations with NWP for improved wind-electric power forecasts[J]. IEEE Transactions on Industrial Informatics, 2016, 12(3):991-1004.

SHENG Yiwei, WANG Han, YAN Jie, et al. Short-term wind power prediction method based on deep clustering-improved temporal convolutional network[J]. Energy Reports, 2023, 9:2118-2129.

刘丽珺, 梁友嘉. 集成CFD与Kalman滤波的微尺度风电场风功率预报方法[J]. 高原气象, 2018, 37(4):1061-1073. LIU Lijun, LIANG Youjia. Wind power prediction method for micro-scale wind farm based on CFD and Kalman filtering integrated correction[J]. Plateau Meteorology, 2018, 37(4):1061-1073.

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