基于敏感性因素分析的数值天气预报修正方法

丛智慧; 刘倩; 刘永前; 韩爽

doi:10.16513/j.2096-2185.DE.2008007

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分布式能源 ›› 2020, Vol. 5 ›› Issue (5) : 8-15. DOI: 10.16513/j.2096-2185.DE.2008007

学术研究

基于敏感性因素分析的数值天气预报修正方法

丛智慧 ¹ ,
刘倩 ² ,
刘永前 ³ ,
韩爽 ³

作者信息 +

Correction Method of Numerical Weather Prediction Based on Sensitivity Factor Analysis

Author information +

文章历史 +

摘要

模型输出统计作为数值天气预报(numerical weather prediction，NWP)修正常用的方法，可有效修正系统误差，节省计算资源和时间。为了提高NWP的预测精度，从影响NWP精度的因素出发，分析不同因素对NWP误差的影响模式，得到对NWP风速误差影响最大的因素。然后以该敏感因素为划分依据建立分段修正模型提高NWP的准确性。为防止研究结果依赖于某种特定建模方法，采用4种常用的统计方法建立模型。算例结果表明：NWP风速误差对风向最为敏感，以风向为划分依据建立的修正模型精度最高。以线性回归方法为例，以风向为划分依据比以风速和气压为划分依据的NWP误差分别降低了3.6%和5.3%。

Abstract

As a common method of numerical weather prediction (NWP) correction, model output statistics can effectively correct system errors and save computing resources and time. In order to improve the prediction accuracy of NWP, this paper analyzes the influence modes of different factors on NWP error, Starting from the factors that affect the accuracy of NWP, and obtains the factors that have the greatest impact on NWP wind speed error. Then based on the sensitive factors, a piecewise correction model is established to improve the accuracy of NWP. In order to prevent the research results from relying on a specific modeling method, four commonly used statistical methods are used to establish the model. The results of numerical examples show that the NWP wind speed error is the most sensitive to the wind direction, and the modified model based on the wind direction has the highest accuracy. Taking the linear regression method as an example, the NWP error based on wind direction is 3.6% and 5.3% lower than that based on wind speed and air pressure.

导出引用

丛智慧, 刘倩, 刘永前, 等. 基于敏感性因素分析的数值天气预报修正方法[J]. 分布式能源. 2020, 5(5): 8-15 https://doi.org/10.16513/j.2096-2185.DE.2008007

Zhihui CONG, Qian LIU, Yongqian LIU, et al. Correction Method of Numerical Weather Prediction Based on Sensitivity Factor Analysis[J]. Distributed Energy Resources. 2020, 5(5): 8-15 https://doi.org/10.16513/j.2096-2185.DE.2008007

中图分类号： TK89

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]	FOLEY A M, LEAHY P G, MARVUGLIA A, et al. Current methods and advances in forecasting of wind power generation[J]. Renewable Energy, 2012, 37(1): 1-8. 本文引用 [1]

[2]	SHEN Xueshun, WANG Jianjie, LI Zechun, et al. Research and operational development of numerical weather prediction in China[J]. Journal of Meteorological Research, 2020, 34(4): 675-698. 本文引用 [1]

[3]	杨正瓴，刘仍祥，李真真. 基于季风和大气压分布的我国风电功率预测研究[J]. 分布式能源，2018, 3(2): 29-38. YANG Zhengling, LIU Rengxiang, LI Zhenzhen. Study on wind power prediction in china based on monsoon and atmospheric pressure distribution[J]. Distributed Energy, 2018, 3(2): 29-38. 本文引用 [1]

[4]	DANFORTH C M, KALNAY E, MIYOSHI T. Estimating and correcting global weather model error[J]. Monthly Weather Review, 2007, 135(2): 281-299. 本文引用 [1]

[5]	YANG Dazhi. On post-processing day-ahead NWP forecasts using Kalman filtering[J]. Solar Energy, 2019, 182: 179-181. 本文引用 [1]

[6]	CAI Haoshu, JIA Xiaodong, FENG Jianshe, et al. A combined filtering strategy for short term and long term wind speed prediction with improved accuracy[J]. Renewable Energy, 2019, 136: 1082-1090. 本文引用 [1]

[7]	LYNCH C, OMAHONY M J, SCULLY T. Simplified method to derive the Kalman filter covariance matrices to predict wind speeds from a NWP model[C]//Energy Procedia. 2014, 62: 676-685. 本文引用 [1]

[8]	郑培，于立军，侯胜亚，等. 基于卡尔曼滤波修正的多步风电功率预测[J]. 热能动力工程，2020, 35(4): 235-241. ZHENG Pei, YU Lijun, HOU Shengya, et al. Multi-step wind power prediction based on Kalman filter correction[J]. Thermal Power Engineering, 2020, 35(4): 235-241. 本文引用 [1]

[9]	YAN Jie, ZHANG Hao, LIU Yongqian, et al. Forecasting the high penetration of wind power on multiple scales using multi-to-multi mapping[J]. IEEE Transactions on Power Systems, 2018, 33(3): 3276-3284. 本文引用 [1]

[10]	HOOLOHAN V, TOMLIN A, COCKERILL T. Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data[J]. Renew Energy, 2018, 126: 1043-1054. 本文引用 [1]

[11]	XU Qianyao, HE Dawei, ZHANG Ning, et al. A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining[J]. IEEE Transactions on Sustainable Energy, 2015, 6(4): 1283-1291. 本文引用 [1]

[12]	GAO Shuang, DONG Lei, LIAO Xiaozhong, et al. Wind power prediction based on multipositon NWP with rough set theory[C]//Control and Decision Conference. IEEE, 2013: 2512-2517. 本文引用 [1]

[13]	LIU Yongqian, WANG Yimei, LI Li, et al. Numerical weather prediction wind correction methods and its impact on computational fluid dynamics based wind power forecasting[J]. Journal of Renewable and Sustainable Energy, 2016, 8(3): 033302. 本文引用 [1]

[14]	YU Chanjong, AHN H, SEOK J. Coordinate-RNN for error correction on numerical weather prediction[C]//IEEE International Conference on Electronics Information and Emergency Communication. IEEE, 2018: 88-90. 本文引用 [1]

[15]	HEWAGE P, TROVATI M, PEREIRA E, et al. Deep learning-based effective fine-grained weather forecasting model [J]. Pattern Analysis and Applications, 2020, early access. 本文引用 [1]

[16]	ZJAVKA L. Wind speed forecast correction models using polynomial neural networks[J]. Renewable Energy, 2015, 83: 998-1006. 本文引用 [1]

[17]	DING Min, ZHOU Hao, XIE Hua, et al. A gated recurrent unit neural networks based wind speed error correction model for short-term wind power forecasting[J]. Neurocomputing, 2019, 365: 54-61. 本文引用 [1]

[18]	HONG S, KU S. Improving wind speed forecasts using deep neural network[J]. The International Journal of Advanced Culture Technology, 2019, 7(4): 338-344. 本文引用 [1]

[19]	ZHAO Xinyu, LIU Jinfu, YU Daren, et al. One-day-ahead probabilistic wind speed forecast based on optimized numerical weather prediction data[J]. Energy Conversion and Management, 2018, 164: 560-569. 本文引用 [1]

[20]	CHU Jingchun, YUAN Ling, PAN Lei, et al. NWP combination correction model based on variable-weight stacking algorithm[C]//Energy Procedia. 2019, 158: 6309-6314. 本文引用 [1]

[21]	WANG Han, HAN Shuang, LIU Yongqian, et al. Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system[J]. Applied Energy, 2019, 237: 1-10. 本文引用 [1]

[22]	DONG Lei, REN Liang, GAO Shuang, et al. Studies on wind farms ultra-short term NWP wind speed correction methods[C]//Chinese Control and Decision Conference. IEEE, 2013: 1576-1579. 本文引用 [1]