地球科学进展

地球科学进展 ›› 2026, Vol. 41 ›› Issue (1): 73 -86. doi: 10.11867/j.issn.1001-8166.2026.013

新型能源系统中气象技术的创新与应用专栏 上一篇    下一篇

风能和太阳能短中期气象预报技术及其最新进展
潘林林1,2(), 靳双龙1,2, 宋宗朋1,2, 丁煌1,2, 胡睿1,2, 肖子牛3,4, 杜杰5, 杨静6, 包庆3,4, 王勃1,2   
  1. 1.中国电力科学研究院新能源研究所，北京 100192
    2.中国电力科学研究院 可再生能源并网全国重点 实验室，北京 100192
    3.中国科学院大气物理研究所 大气科学和地球流体力学 数值模拟重点实验室，北京 100029
    4.中国科学院大学，北京 100049
    5.南京信息工程大学 计算机与软件学院，江苏 南京 210044
    6.北京师范大学 地理科学学部，北京 100875
  • 收稿日期:2025-11-11 修回日期:2025-12-30 出版日期:2026-01-10
  • 基金资助:
    国家电网公司海外高层次人才专项(5100-202455418A-3-5-YS)

Short- and Medium-Term Meteorological Forecasting Technologies for Wind and Solar Energy and Their Latest Advances

Linlin Pan1,2(), Shuanglong Jin1,2, Zongpeng Song1,2, Huang Ding1,2, Rui Hu1,2, Ziniu Xiao3,4, Jie Du5, Jing Yang6, Qing Bao3,4, Bo Wang1,2   

  1. 1.Department of Renewable Energy, China Electric Power Research Institute, Beijing 100192, China
    2.State Key Laboratory of Renewable Energy Grid Integration, China Electric Power Research Institute, Beijing 100192, China
    3.Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
    4.University of Chinese Academy of Sciences, Beijing 100049, China
    5.School of Computer Science and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
    6.Faculty of Geophysical Science, Beijing Normal University, Beijing 100875, China
  • Received:2025-11-11 Revised:2025-12-30 Online:2026-01-10 Published:2026-03-10
  • About author:Pan Linlin, research areas include renewable energy data assimilation, numerical weather prediction, and physical mechanism research. E-mail: panlinlin@epri.sgcc.com.cn
  • Supported by:
    the State Grid Corporation of China Overseas High-Level Talent Special Project(5100-202455418A-3-5-YS)
全文 ( 28 ) 下载PDF ( 1155 )

随着风能和太阳能发电技术的迅猛发展，其在电力系统中的占比持续提升。然而，这两种能源的发电量直接受天气状况影响，具有显著的随机性、波动性和间歇性，这给电力调度和电网的安全稳定运行带来了严峻挑战。高精度的功率预测技术是应对这一挑战、实现风能和太阳能发电高效利用的关键。系统回顾了风能和太阳能短中期气象预报技术的发展历程、核心技术及其最新研究进展。首先，对国内外相关文献的综合分析表明，传统的风能和太阳能预报方法，如数值天气预报方法、统计方法（包括时间序列分析和机器学习）以及二者结合的混合/统计后处理方法，已无法完全满足当前需求。而通过在高分辨率快速循环预报、能源专用物理过程优化、混合集合卡曼滤波与四维变分同化等关键领域开展协同优化，预报的准确性与可靠性得到了显著提升。此外，新一代动力框架与先进人工智能大模型的引入、多源数据的同化融合以及“数字孪生”等新兴技术的应用，为进一步优化预报结果提供了新的可能。最后，分析了当前预报技术面临的主要挑战，并对未来发展方向进行了展望，重点围绕极端天气精准预报等关键问题；通过嵌入物理规律发展“物理信息神经网络”；由传统物理模型与大模型成员构成的集合概率预报将得到普及；量子计算等颠覆性技术有望推动超高分辨率气象模拟成为现实；实现预报技术与电网自动发电控制等场景的深度耦合，构建闭环智能决策系统。旨在为能源气象领域的研究人员和工程技术人员提供技术参考。

关键词: 风能预报, 太阳能预报, 数据同化融合, 数值天气预报, 人工智能深度学习

With the rapid advancement of wind and solar power generation technologies, the proportion of wind and solar energy within power systems continues to rise. However, the output of these two energy sources is directly influenced by weather conditions, exhibiting significant randomness, volatility, and intermittency. This poses severe challenges to power dispatch and the secure, stable operation of the grid. High-precision power forecasting technologies at short- and medium-term time scales are crucial for addressing these challenges and achieving efficient utilization of wind and solar power generation. This paper systematically reviews the developmental trajectory, core technologies, and latest research advancements in short- and medium-term meteorological forecasting for wind and solar energy. First, a comprehensive analysis of relevant domestic and international literature indicates that traditional wind and solar energy forecasting methods—such as numerical weather prediction techniques, statistical approaches (including time series analysis and machine learning), and hybrid/statistical post-processing methods combining both—no longer fully meet current demands. Significant improvements in forecast accuracy and reliability have been achieved through synergistic optimization in key areas: high-resolution rapid-cycle forecasting, energy-specific physical process optimization, and hybrid data assimilation method of Ensemble Kalman Filtering (EnKF) and four-dimensional variational assimilation (4D-Var). Furthermore, the introduction of next-generation dynamic frameworks and advanced artificial intelligence (AI) large models, the assimilation and fusion of multi-source data, and the application of emerging technologies like “digital twins” offer new avenues for further refining forecast outcomes. Finally, the paper analyses the challenges confronting current forecasting techniques and outlines future development directions which include but not limited to challenges posed by forecasting extreme weather events，complex terrain physical and dynamical representations, enhancing model interpretability, and subseasonal to seasonal-scale forecasting; Deep integration of artificial intelligence with physical models to develop “physical-information neural networks” by embedding physical laws; Collective probabilistic forecasting that combines traditional physical models with large AI model ensembles will become widespread; Disruptive technologies like quantum computing are poised to advance ultra-high-resolution meteorological simulations; Achieving deep coupling between forecasting technologies and scenarios such as the grid-based automatic power generation control to build closed-loop intelligent decision-making systems. This review aims to provide a technical reference for researchers and engineering technicians in the field of energy meteorology.

Key words: Wind energy forecasting, Solar energy forecasting, Data assimilation and fusion, Numerical weather prediction, Artificial intelligence and deep learning

中图分类号: 

图1 风电与光伏电并网对电网稳定性影响的示意图
Fig. 1 Schematic of the impact of wind power and photovoltaic power grid integration on grid stability
图2 CAISO“鸭子曲线”示意图［7］
Fig. 2 Schematic diagram of the California Independent System Operator （CAISO） duck curve［7］
图3 数值天气预报工作流程示意图
Fig. 3 Schematic of numerical weather prediction workflow
[1] International Renewable Energy Agency （IRENA）. Renewable energy statistics 2025 ［R/OL］. IRENA Publications， 2025-09-04. .
[2] International Energy Agency （IEA）. Renewables 2024 analysis and forecast to 2030 ［R/OL］. IEA Publications， 2024-10-31. .
[3] National Energy Administration of China. National electric power industry statistics from January to June 2025 ［EB/OL］. 2025. ［2025-10-20］. .
中国国家能源局. 2025年1月至6月全国电力工业统计数据，2025. ［2025-10-20］. .
[4] National Renewable Energy Laboratory （NREL）. ERCOT event on February 26， 2008： Lessons learned ［R/OL］. Technical Report， NREL/TP-500-43373， 2008-02-26. .
[5] Zhang Y， Wang J X， Wang X F. Review on probabilistic forecasting of wind power generation［J］. Renewable and Sustainable Energy Reviews， 2014， 32： 255-270.
[6] González-Aparicio I， Zucker A. Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain［J］. Applied Energy， 2015， 159： 334-349.
[7] U.S. Energy Information Administration （EIA）. As solar capacity grows， duck curves are getting deeper in California ［EB/OL］. 2023. ［2025-10-25］. .
[8] California Independent System Operator. Flexible resources help renewables： fast facts. CAISO ［R/OL］. 2022-08-07. .
[9] Xu Lina， Shen Yanbo， Wang Jieru. Characteristic analysis and threshold determination of power ramp events in a wind farm of Inner Mongolia［J］. Acta Energiae Solaris Sinica， 2025， 46（9）： 133-142.
徐丽娜， 申彦波， 王捷儒. 内蒙古某风电场爬坡事件特征分析及阈值确定［J］. 太阳能学报， 2025， 46（9）： 133-142.
[10] Warner T T. Numerical weather and climate prediction［M］. Cambridge， UK： Cambridge University Press， 2010.
[11] Stauffer D R， Seaman N L. Use of four-dimensional data assimilation in a limited-area mesoscale model. part I： experiments with synoptic-scale data［J］. Monthly Weather Review， 1990， 118（6）： 1 250-1 277.
[12] Liu Y B， Warner T T， Bowers J F， et al. The operational mesogamma-scale analysis and forecast system of the U.S. army test and evaluation command. part I： overview of the modeling system， the forecast products， and how the products are used［J］. Journal of Applied Meteorology and Climatology， 2008， 47（4）： 1 077-1 092.
[13] Pan L L， Liu Y B， Liu Y W， et al. Impact of Four-Dimensional Data Assimilation （FDDA） on urban climate analysis［J］. Journal of Advances in Modeling Earth Systems， 2015， 7（4）： 1 997-2 011.
[14] Pan L L， Liu Y B， Knievel J， et al. Evaluations of WRF sensitivities in surface simulations with an ensemble prediction system［J］. Atmosphere， 2018， 9（3）： 106.
[15] Pan L L， Liu Y B， Roux G， et al. Seasonal variation of the surface wind forecast performance of the high-resolution WRF-RTFDDA system over China［J］. Atmospheric Research， 2021， 259： 105673.
[16] Liu J J， Wang B， Wang S D. The structure of background-error covariance in a four-dimensional variational data assimilation system： single-point experiment［J］. Advances in Atmospheric Sciences， 2010， 27（6）： 1 303-1 310.
[17] Rabier F， Järvinen H， Klinker E， et al. The ECMWF operational implementation of four-dimensional variational assimilation. I： experimental results with simplified physics［J］. Quarterly Journal of the Royal Meteorological Society， 2000， 126（564）： 1 143-1 170.
[18] Ivanova T， Porchetta S， Buckingham S， et al. Improving wind and power predictions via four-dimensional data assimilation in the WRF model： case study of storms in February 2022 at Belgian offshore wind farms［J］. Wind Energy Science， 2025， 10（1）： 245-268.
[19] Hunt B R， Kostelich E J， Szunyogh I. Efficient data assimilation for spatiotemporal chaos： a local ensemble transform Kalman filter［J］. Physica D： Nonlinear Phenomena， 2007， 230（1/2）： 112-126.
[20] Shao C L， Nerger L. WRF-PDAF v1.0： implementation and application of an online localized ensemble data assimilation framework［J］. Geoscientific Model Development， 2024， 17（10）： 4 433-4 445.
[21] Optis M， Monahan A， Chan H J C K. Evaluation of WRF hub-height wind forecasts over complex terrain ［J］. Wind Energy Science， 2016， 1（2）： 207-224.
[22] ECMWF. IFS Documentation CY48R1 ［R］. ECMWF， 2023.
[23] Cai Q R， Qing J， Zhong C Y， et al. Temporal and spatial heterogeneity analysis of wind and solar power complementarity and source-load matching characteristics in China［J］. Energy Conversion and Management， 2024， 315： 118770.
[24] Kim J， Park S， Lee H， et al. Impact of physical parameterizations on wind simulation with WRF V3.7.1 ［J］. Geoscientific Model Development， 2022， 15（22）： 8 111-8 130.
[25] Perez R， Ineichen P， Moore K， et al. A new operational model for satellite-derived irradiances： description and validation［J］. Solar Energy， 2002， 73（5）： 307-317.
[26] Jiménez P A， Hacker J P， Dudhia J， et al. Description and clear-sky assessment of an augmented NWP Model for Solar power prediction［J］. Bulletin of the American Meteorological Society， 2016， 97： 1 249-1 264.
[27] Zhang W H， Tian M， Hai S F， et al. Improving the forecasts of coastal wind speeds in Tianjin， China based on the WRF model with machine learning algorithms［J］. Journal of Meteorological Research， 2024， 38（3）： 570-585.
[28] Wulayin Y， Li H Q， Zhang L， et al. Evaluating the prediction performance of the WRF-CUACE model in Xinjiang， China［J］. Remote Sensing， 2024， 16（19）： 3747.
[29] LIU H， WANG J， ZHANG Z， et al. Improving wind speed forecasts at wind turbine locations over Northern China ［J］. Weather and Forecasting， 2022， 37（9）： 1 641-1 658.
[30] Kusiak A， Zhang Z J， Verma A. Prediction， operations， and condition monitoring in wind energy［J］. Energy， 2013， 60： 1-12.
[31] 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.
[32] Chow C W， Urquhart B， Lave M， et al. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed［J］. Solar Energy， 2011， 85（11）： 2 881-2 893.
[33] Chen S L， Liang Z J， Guo S， et al. Estimation of high-resolution solar irradiance data using optimized semi-empirical satellite method and GOES-16 imagery［J］. Solar Energy， 2022， 241： 404-415.
[34] Ma J W， Du J， Chen Q X， et al. Multi-feature extraction spatio-temporal interaction graph network for wind speed forecasting in windfarm［J］. Energy， 2025， 333： 137229.
[35] Du J， Chen S Z， Pan L L， et al. A wind speed prediction method based on signal decomposition technology deep learning model［J］. Energies， 2025， 18（5）： 1136.
[36] Lange M， Focken U. Physical approach to short-term wind power prediction［M］. Heidelberg： Springer， 2005
[37] Glahn H R， Lowry D A. The use of Model Output Statistics （MOS） in objective weather forecasting［J］. Journal of Applied Meteorology， 1972， 11（8）： 1 203-1 211.
[38] Chen T C， Penny S G， Whitaker J S， et al. Correcting systematic and state-dependent errors in the NOAA FV3-GFS using neural networks［J］. Journal of Advances in Modeling Earth Systems， 2022， 14（11）： e2022MS003309.
[39] Xu J J， Xiao Z N， Lin Z H， et al. System bias correction of short-term hub-height wind forecasts using the Kalman filter［J］. Protection and Control of Modern Power Systems， 2021， 6： 37.
[40] Xu Jingjing， Hu Fei， Xiao Ziniu， et al. Analog bias correction of numerical model on wind power prediction［J］. Journal of Applied Meteorological Science， 2013， 24（6）： 731-740.
徐晶晶， 胡非， 肖子牛， 等. 风能模式预报的相似误差订正［J］. 应用气象学报， 2013， 24（6）： 731-740.
[41] Taha A L， Nazih N， Makeen P. Wind speed prediction based on variational mode decomposition and advanced machine learning models in zaafarana， Egypt［J］. Scientific Reports， 2025， 15： 15599.
[42] Zhang Yubing， Shen Yanbo， Yao Xin， et al. Comparative study of different decision tree algorithms for PV suitability evaluation in Qinghai plateau［J］. Acta Energiae Solaris Sinica， 2024， 45（12）： 30-39.
张玉冰， 申彦波， 姚鑫， 等. 青海高原光伏适宜性评价的不同决策树算法的比较研究［J］. 太阳能学报， 2024， 45（12）： 30-39.
[43] Li Y， Tang F， Gao X， et al. Numerical weather prediction correction strategy for short-term wind power forecasting based on bidirectional gated recurrent unit and XGBoost［J］. Frontiers in Energy Research， 2022， 9： 836144.
[44] Jiménez P A， Dudhia J， Thompson G， et al. Improving the cloud initialization in WRF-Solar with enhanced short-range forecasting functionality： the MAD-WRF model［J］. Solar Energy， 2022， 239： 221-233.
[45] Zhang J， Zhu X C， Xie Y G， et al. Detection and prediction of wind and solar photovoltaic power ramp events based on data-driven methods： a critical review［J］. Energies， 2025， 18（13）： 3290.
[46] Benjamin S G， Weygandt S S， Brown J M， et al. A North American hourly assimilation and model forecast cycle： the rapid refresh［J］. Monthly Weather Review， 2016， 144（4）： 1 669-1 694.
[47] Carvalho D， Rocha A， Gómez-Gesteira M， et al. A sensitivity study of the WRF model in wind simulation for an area of high wind energy［J］. Environmental Modelling & Software， 2012， 33： 23-34.
[48] Giebel G， Brownsword R， Kariniotakis G， et al. The state-of-the-art in short-term prediction of wind power： a literature overview ［M］. Portugal： ANEMOS.plus， 2011.
[49] Mazzaro L J， Muñoz-Esparza D， Lundquist J K， et al. Nested mesoscale-to-LES modeling of the atmospheric boundary layer in the presence of under-resolved convective structures［J］. Journal of Advances in Modeling Earth Systems， 2017， 9（4）： 1 795-1 810.
[50] Shen Yanbo， Zhang Shunqian， Guo Peng， et al. Calculation for solar energy resource based on smarts mode： as a case study in Sichuan Province［J］. Acta Energiae Solaris Sinica， 2016， 37（2）： 482-488.
申彦波， 张顺谦， 郭鹏， 等. 基于SMARTS模式的太阳能资源计算： 以四川省为例［J］. 太阳能学报， 2016， 37（2）： 482-488.
[51] Inman R H， Pedro H T C， Coimbra C F M. Solar forecasting methods for renewable energy integration［J］. Progress in Energy and Combustion Science， 2013， 39（6）： 535-576.
[52] Jimenez P A， Hacker J P， Dudhia J， et al. WRF-solar： description and clear-sky assessment of an augmented NWP model for solar power prediction［J］. Bulletin of the American Meteorological Society， 2016， 97（7）： 1 249-1 264.
[53] Ruiz-Arias J A， Alonso-Montesinos J， Lara-Fanjul L， et al. Evaluation of the WRF-Solar model’s performance for GHI and DNI forecasting in a desert environment ［J］. Solar Energy， 2016， 133（1）： 437-453.
[54] Olson J B， Kenyon J S， Delle M L， et al. The impact of planetary boundary layer and land surface physics schemes on wind forecasts for energy applications ［J］. Weather and Forecasting， 2019， 34（5）： 1 453-1 473.
[55] Bernardet L， Bengtsson L， Reinecke P A， et al. Common community physics package： fostering collaborative development in physical parameterizations and suites［J］. Bulletin of the American Meteorological Society， 2024， 105（8）： E1490-E1505.
[56] Razagui A， Abdeladim K， Bouchouicha K， et al. A new approach to forecast solar irradiances using WRF and libRadtran models， validated with MERRA-2 reanalysis data and pyranometer measures［J］. Solar Energy， 2021， 221： 148-161.
[57] Feng J， Huang X Y， Li Y J. Improving surface wind speed forecasts using an offline surface multilayer model with optimal ground forcing［J］. Journal of Advances in Modeling Earth Systems， 2022， 14（10）： e2022MS003072.
[58] MANIKIN G S， BENTLEY A M， DAWSON L C， et al. An overview of the Global Ensemble Forecast System （GEFS） version 12 official evaluation ［C］// American Meteorological Society Meeting Abstracts. 2021， 101： 6.1-6.2.
[59] De Rosnay P， Browne P， De Boisséson E， et al. Coupled data assimilation at ECMWF： current status， challenges and future developments［J］. Quarterly Journal of the Royal Meteorological Society， 2022， 148（747）： 2 672-2 702.
[60] Geer A J， Lonitz K， Weston P， et al. All-sky satellite data assimilation at operational weather forecasting centres［J］. Quarterly Journal of the Royal Meteorological Society， 2018， 144（713）： 1 191-1 217.
[61] Bianco L， Dima J， Schwartz C S， et al. A comparison of the impact of assimilating remotely sensed and in situ observations on WRF-based hub-height wind forecasts ［J］. Boundary-Layer Meteorology， 2016， 158（2）： 231-262.
[62] The Joint Center for Satellite Data Assimilation （JCSDA）. JEDI documentations ［R/OL］. 2024-10-09. .
[63] Ren Y， Suganthan P N， Srikanth N. Ensemble methods for wind and solar power forecasting： a state-of-the-art review［J］. Renewable and Sustainable Energy Reviews， 2015， 50： 82-91.
[64] Pinson P， Madsen H， Nielsen H A， et al. From probabilistic forecasts to statistical scenarios of short-term wind power production［J］. Wind Energy， 2009， 12（1）： 51-62.
[65] Gneiting T， Raftery A E. Strictly proper scoring rules， prediction， and estimation［J］. Journal of the American Statistical Association， 2007， 102（477）： 359-378.
[66] Mahoney W P， Parks K， Wiener G， et al. A wind power forecasting system to optimize grid integration［J］. IEEE Transactions on Sustainable Energy， 2012， 3（4）： 670-682.
[67] Kritzinger W， Karner M， Traar G， et al. Digital twin in manufacturing： a categorical literature review and classification［J］. IFAC-PapersOnLine， 2018， 51（11）： 1 016-1 022.
[68] Harris L M， Lin S J. A two-way nested global-regional dynamical core on the cubed-sphere grid［J］. Monthly Weather Review， 2013， 141（1）： 283-306.
[69] Park S H， Klemp J B， Skamarock W C. A comparison of mesh refinement in the global MPAS-A and WRF models using an idealized normal-mode baroclinic wave simulation［J］. Monthly Weather Review， 2014， 142（10）： 3 614-3 634.
[70] Zängl G， Reinert D， Rípodas P， et al. The ICON （ICOsahedral non-hydrostatic） modelling framework of DWD and MPI-M： description of the non-hydrostatic dynamical core［J］. Quarterly Journal of the Royal Meteorological Society， 2015， 141（687）： 563-579.
[71] China Meteorological Administration. The China Meteorological Administration releases the next-generation atmospheric numerical model ［EB/OL］. 2025. ［2025-10-21］. .
中国气象局. 中国气象局下一代大气数值模式发布.2025. ［2025-10-21］. .
[72] Price I， Sanchez-Gonzalez A， Alet F， et al. Probabilistic weather forecasting with machine learning［J］. Nature， 2025， 637（8 044）： 84-90.
[73] 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.
[74] He Y T， Gao Q Z， Jin Y Y， et al. Short-term photovoltaic power forecasting method based on convolutional neural network［J］. Energy Reports， 2022， 8： 54-62.
[75] Qureshi A S， Khan A， Zameer A， et al. Wind power prediction using deep neural network based meta regression and transfer learning［J］. Applied Soft Computing， 2017， 58： 742-755.
[76] Pan X X， Wang L， Wang Z J， et al. Short-term wind speed forecasting based on spatial-temporal graph transformer networks［J］. Energy， 2022， 253： 124095.
[77] Lv Y L， Hu Q， Xu H， et al. An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model［J］. Energy， 2024， 293： 130751.
[78] Yang L， Tsung F， Wang K B， et al. Wind power forecasting based on a spatial-temporal graph convolution network with limited engineering knowledge［J］. IEEE Transactions on Instrumentation and Measurement， 2024， 73： 1-13.
[79] Shobanke M， Bhatt M， Shittu E. Advancements and future outlook of Artificial Intelligence in energy and climate change modeling［J］. Advances in Applied Energy， 2025， 17： 100211.
[80] Camps-Valls G， Fernández-Torres M Á， Cohrs K H， et al. Artificial intelligence for modeling and understanding extreme weather and climate events［J］. Nature Communications， 2025， 16： 1919.
[81] Bi K F， Xie L X， Zhang H H， et al. Accurate medium-range global weather forecasting with 3D neural networks［J］. Nature， 2023， 619（7 970）： 533-538.
[82] Chen K， Han T， Ling F H， et al. The operational medium-range deterministic weather forecasting can be extended beyond a 10-day lead time［J］. Communications Earth & Environment， 2025， 6： 518.
[83] Lam R， Sanchez-Gonzalez A， Willson M， et al. Learning skillful medium-range global weather forecasting［J］. Science， 2023， 382（6 677）： 1 416-1 421.
[84] Pathak J， Subramanian S， Harrington P， et al. FourCastNet： a global data-driven high-resolution weather model using adaptive fourier neural operators ［PP/OL］. ［2025-10-21］. .
[85] Shen Z X， Sun Q M， Lu X Y， et al. Current progress in subseasonal-to-decadal prediction based on machine learning［J］. Applied Computing and Geosciences， 2024， 24： 100201.
[86] Lledó L， Torralba V， Soret A， et al. Seasonal forecasts of wind power generation［J］. Renewable Energy， 2019， 143： 91-100.
[87] Wang J X， Chen L D， Tan Z F， et al. Inherent spatiotemporal uncertainty of renewable power in China［J］. Nature Communications， 2023， 14： 5379.
[88] Raissi M， Perdikaris P， Karniadakis G E. Physics-informed neural networks： a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations［J］. Journal of Computational Physics， 2019， 378： 686-707.
[89] Kashinath K， Mustafa M， Albert A， et al. Physics-informed machine learning： case studies for weather and climate modelling［J］. Philosophical Transactions of the Royal Society A： Mathematical， Physical and Engineering Sciences， 2021， 379（2 194）： 20200093.
[90] Ren H L， Bao Q， Zhou C G， et al. Seamless prediction in China： a review［J］. Advances in Atmospheric Sciences， 2023， 40（8）： 1 501-1 520.
[91] Yang J， Lu M Q， Liu A L， et al. The “last-mile efforts” of subseasonal prediction and services for climate resilience and sustainability： review and outlook［J］. Advances in Atmospheric Sciences， 2026， 43（6）： 1-10.
[92] Liu F， Zhou J H， Wang B， et al. Opportunities and barriers for skillful subseasonal prediction of East Asian summer precipitation［J］. Bulletin of the American Meteorological Society， 2024， 105（11）： E2216-E2230.
[93] Pinson P. Wind energy： forecasting challenges for its operational management［J］. Statistical Science， 2013， 28（4）： 564-585.
[94] Mancarella P. MES （multi-energy systems）： an overview of concepts and evaluation models［J］. Energy， 2014， 65： 1-17.
[95] Peng D G， Liu Y， Wang D H， et al. Multi-energy load forecasting for integrated energy system based on sequence decomposition fusion and factors correlation analysis［J］. Energy， 2024， 308： 132796.
[1] 潘留杰, 张宏芳, 刘嘉慧敏, 祁春娟, 梁绵, 马丹萌, 李培荣, 戴昌明, 高星星. MODE检验在天气预报中的应用研究进展[J]. 地球科学进展, 2024, 39(2): 193-206.
[2] 宋淑丽;朱文耀;廖新浩. 地基GPS气象学研究的主要问题及最新进展[J]. 地球科学进展, 2004, 19(2): 250-259.
[3] 谢志辉，丑纪范. 大气动力学方程组全局分析的研究进展[J]. 地球科学进展, 1999, 14(2): 133-139.
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