基于WRF模式的中长期风速预报及订正研究

张富强; 金春伟; 周胡; 陆艳艳; 杨树峰; 聂高臻

doi:10.19912/j.0254-0096.tynxb.2024-1591

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (1) : 585-592. DOI: 10.19912/j.0254-0096.tynxb.2024-1591

基于WRF模式的中长期风速预报及订正研究

张富强¹, 金春伟², 周胡², 陆艳艳², 杨树峰³, 聂高臻⁴

作者信息 +

RESEARCH ON MEDIUM AND LONG TERM WIND SPEED FORECASTING AND CORRECTION BASED ON WRF MODEL

Zhang Fuqiang¹, Jin Chunwei², Zhou Hu², Lu Yanyan², Yang Shufeng³, Nie Gaozhen⁴

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文章历史 +

摘要

采用5种参数化方案对中国东部沿海地区春夏季10 m风速进行预报,基于最优方案预报风速及相应观测风速构建随机森林订正模型,并用地面实况观测数据对预报模型进行检验。结果表明,各方案对10 m风速的模拟效果相似,且模拟风速均大于观测风速,参数化方案P5对研究区域10 m风速的预报效果最好,风速预报准确率最高,为38%。经随机森林订正后,风速预报准确率提升至53%,订正效果显著,且对内陆地区的订正效果优于近海地区。

Abstract

The mid-long term prediction of wind speed plays an important support for wind power generation and dispatching. Due to the highly variable and gusty characteristics of wind speed, exploring methods to improve wind speed forecasts has important practical significance. In this research, five parameterization schemes were used to predict the 10 m wind speed in spring and summer in the coastal areas of eastern China. A random-forest-based correction model was constructed based on the wind speed predicted by the optimal scheme and the corresponding observed wind speed. The results show that the performance of each WRF experiment on predicting the 10 m wind speed are close, and the simulated wind speed is greater than the observed wind speed. The P5 parameterization scheme yields the best performance for the 10 m wind speed prediction over the study area, with the highest wind speed prediction accuracy of 38%. After corrected by the random forest model, the wind speed prediction accuracy is increased to 53%, indicating that the correction effect is significant. Moreover, the correction improves more over the inland regions than over the offshore areas.

导出引用

张富强, 金春伟, 周胡, 陆艳艳, 杨树峰, 聂高臻. 基于WRF模式的中长期风速预报及订正研究[J]. 太阳能学报. 2026, 47(1): 585-592 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1591

Zhang Fuqiang, Jin Chunwei, Zhou Hu, Lu Yanyan, Yang Shufeng, Nie Gaozhen. RESEARCH ON MEDIUM AND LONG TERM WIND SPEED FORECASTING AND CORRECTION BASED ON WRF MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 585-592 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1591

中图分类号： TM614

参考文献

[1] 詹浩. 基于WRF的中国西南某复杂山区风资源评估[D]. 重庆: 重庆大学, 2020.ZHAN H. WRF-based assessment of wind resource in a complex mountainous area in southwest China[D]. Chongqing: Chongqing University, 2020.
[2] 周鹤良. 我国新能源风电产业实现跨越式发展[J]. 电气时代, 2019(8): 20-22, 24.ZHOU H L. China’s new energy wind power industry has achieved leap-forward development[J]. Electric age, 2019(8): 20-22, 24.
[3] CHEN K L, YU J.Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach[J]. Applied energy, 2014, 113: 690-705.
[4] 罗文, 王莉娜. 风场短期风速预测研究[J]. 电工技术学报, 2011, 26(7): 68-74.LUO W, WANG L N. Short-term wind speed forecasting for wind farm[J]. Transactions of China Electrotechnical Society, 2011, 26(7): 68-74.
[5] JAMALUDIN A R, YUSOF F, KANE I L, et al.A comparative study between conventional ARMA and Fourier ARMA in modeling and forecasting wind speed data[J]. AIP conference proceedings, 2016, 1750: 060022.
[6] 吴肃霜, 蔡小路, 李俊贤. 风电功率预测方法与最新技术发展研究[J]. 水电与新能源, 2024(3): 38-41, 45.WU S S, CAI X L, LI J X. On the wind power prediction methods and the latest technological development[J]. Hydropower and new energy, 2024(3): 38-41, 45.
[7] 孙全德, 焦瑞莉, 夏江江, 等. 基于机器学习的数值天气预报风速订正研究[J]. 气象, 2019, 45(3): 426-436.SUN Q D, JIAO R L, XIA J J, et al. Adjusting wind speed prediction of numerical weather forecast model based on machine learning methods[J]. Meteorological monthly, 2019, 45(3): 426-436.
[8] CHENG C S, YANG S F, ZHU J, et al. Reconstruction of near-surface hourly wind speed data sets during the period1959—2021 over China[J]. Earth and space science, 2023, 10(11): e2023EA003295.
[9] 叶小岭, 顾荣, 邓华, 等. 基于WRF模式和PSO-LSSVM的风电场短期风速订正[J]. 电力系统保护与控制, 2017, 45(22): 48-54.YE X L, GU R, DENG H, et al. Modification technology research of short-term wind speed in wind farm based on WRF model and PSO-LSSVM method[J]. Power system protection and control, 2017, 45(22): 48-54.
[10] 向玲, 邓泽奇. 基于改进经验小波变换和最小二乘支持向量机的短期风速预测[J]. 太阳能学报, 2021, 42(2): 97-103.XIANG L, DENG Z Q. Short-term wind speed forecasting based on improved empirical wavelet transform and least squares support vector machines[J]. Acta energiae solaris sinica, 2021, 42(2): 97-103.
[11] 毕贵红, 赵鑫, 李璐, 等. 双模式分解CNN-LSTM集成的短期风速预测模型[J]. 太阳能学报, 2023, 44(3): 191-197.BI G H, ZHAO X, LI L, et al. Dual-mode decomposition CNN-LSTM integrated short-term wind speed forecasting model[J]. Acta energiae solaris sinica, 2023, 44(3): 191-197.
[12] SATHYARAJ J, SANKARDOSS V.A novel analysis of random forest regression model for wind speed forecasting[J]. Cogent engineering, 2024, 11(1): 2434617.
[13] SAHA S, MOORTHI S, WU X R, et al.The NCEP climate forecast system version 2[J]. Journal of climate, 2014, 27(6): 2185-2208.
[14] 肖颖, 庞轶舒, 马振峰, 等. NCEP CFSv2模式对川渝夏季降水次季节预测技巧评估及预报偏差分析[J]. 高原气象, 2023, 42(6): 1576-1588.XIAO Y, PANG Y S, MA Z F, et al. Sub-seasonal forecasting skills assessment and deviation analysis of CFSv2 for summer precipitation in Sichuan and Chongqing[J]. Plateau meteorology, 2023, 42(6): 1576-1588.
[15] 张琛, 廖婷婷, 孙扬, 等. 基于机器学习算法的精细化流场模拟研究[J]. 环境科学学报, 2022, 42(2): 318-331.ZHANG C, LIAO T T, SUN Y, et al. Research on refined flow field simulation based on machine learning methods[J]. Acta scientiae circumstantiae, 2022, 42(2): 318-331.
[16] 于淼, 廉丽姝, 李宝富, 等. 基于WRF模式的青岛沿海地区近地面风的敏感性研究[J]. 热带气象学报, 2020, 36(2): 277-288.YU M, LIAN L S, LI B F, et al. Sensitivity analysis of near-surface wind in Qingdao coastal area based on WRF model[J]. Journal of tropical meteorology, 2020, 36(2): 277-288.
[17] 黄俊辉, 孙文涛, 李辰, 等. 基于WRF模式的海上风能资源特性分析及评价[J]. 太阳能学报, 2021, 42(7): 278-283.HUANG J H, SUN W T, LI C, et al. Analysis and evaluation of offshore wind energy resources characteristics based on WRF model[J]. Acta energiae solaris sinica, 2021, 42(7): 278-283.
[18] 葛苏鞍, 李阳, 杨仕轩, 等. WRF模式中参数化方案对新疆北部不同地形地区风场模拟的影响[J]. 科学技术与工程, 2024, 24(31): 13277-13287.GE S A, LI Y, YANG S X, et al. Effects of WRF parameterization schemes on wind field simulation in different terrain areas in northern Xinjiang[J]. Science technology and engineering, 2024, 24(31): 13277-13287.
[19] 王秀智, 杨启东, 何帅辰, 等. WRF模式多参数化方案对东南亚低纬高原陆气耦合强度的模拟评估[J]. 高原气象, 2024, 43(4): 995-1010.WANG X Z, YANG Q D, HE S C, et al. Evaluation of land-atmosphere coupling strength in low-latitude highland of Southeast Asia by WRF model parameterization schemes[J]. Plateau meteorology, 2024, 43(4): 995-1010.
[20] 马晨晨, 余晔, 何建军, 等. 次网格地形参数化对WRF模式在复杂地形区风场模拟的影响[J]. 干旱气象, 2016, 34(1): 96-105, 124.MA C C, YU Y, HE J J, et al. Effect of subgrid-scale terrain parameterization on WRF’s performance on wind field over complex terrain[J]. Journal of arid meteorology, 2016, 34(1): 96-105, 124.
[21] 于丽娟, 尹承美, 林应超, 等. 中国区域近地面风速动力降尺度研究[J]. 干旱气象, 2017(1): 23-28, 56.YU L J, YIN C M, LIN Y C, et al. Study of dynamical downscaling on near surface wind speed over China[J]. Journal of arid meteorology, 2017(1): 23-28, 56.
[22] 郭楚珊, 郭鹏, 杨锡运. 基于三适应度粒子群算法的风速威布尔分布参数估计[J]. 太阳能学报, 2019, 40(1): 206-212.
GUO C S, GUO P, YANG X Y.Wind weibull distribution parameter estimation based on three fitness PSO algorithm[J]. Acta energiae solaris sinica, 2019, 40(1): 206-212.