WRF模式对江苏如东地区风速预报的检验分析

汪君; 王会军

doi:10.3878/j.issn.1006-9585.2013.11152

WRF模式对江苏如东地区风速预报的检验分析

doi: 10.3878/j.issn.1006-9585.2013.11152

汪君¹,
王会军²

1.
中国科学院大气物理研究所竺可桢-南森国际研究中心, 北京100029;中国科学院大学, 北京100049
2.
中国科学院大气物理研究所竺可桢-南森国际研究中心, 北京100029

基金项目: 国家自然科学基金项目41130103

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出版历程
- 收稿日期: 2011-09-22
- 修回日期: 2013-02-17

Forecasting of Wind Speed in Rudong, Jiangsu Province, by the WRF Model

WANG Jun¹,
WANG Huijun²

1.
Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;University of Chinese Academy of Sciences, Beijing 100049
2.
Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

摘要

摘要: 探讨了WRF模式在风电场的风速或者功率预报中应用的可行性, 主要研究和评估了WRF模式对地处东亚季风区及海陆交界的江苏如东地区夏季和冬季风速的短期预报效能。研究发现WRF模式可以比较好地预报如东站冬季的风速, 24 h预报的风速时间序列和观测资料的相关系数可以达到0.61, 通过置信度99%的检验, 48 h和72 h的预报与观测风速相关系数分别为0.54和0.47, 也能通过置信度99%的检验;相对而言, 模式对夏季风速的预报则要差一些, 24 h的相关系数有0.59, 48 h和72 h的相关系数只有0.47和0.30, 但仍能通过置信度99%的检验。在量值上, 模式预报的风速比观测值都略偏大一些。而江苏南通市预报结果显示, 模式的预报效能要比如东稍高一些, 和如东类似, 模式对该地冬季的预报要好于对夏季风速的预报。从更大尺度范围的分析也表明, 模式对不同地区预报的准确度是不一样的, 对海面以及海陆交界的海岸预报精度要高一些, 在平坦的内陆地区预报也比较好, 但在山区预报效能则较差。总体说来, WRF能胜任风速短期预报, 值得进一步研究和应用。
- 风电功率预报 /
- 风速预报 /
- WRF模式
Abstract: To discuss the possibility of applying a numerical Weather Research and Forecasting (WRF) model to wind power forecasting, the summer and winter hourly wind speed forecasting performance of the WRF model used in Rudong, Jiangsu Province, is analyzed in this paper. It is determined that the WRF model can forecast winter wind speed in Rudong 24 h in advance very well with a correlation coefficient as high as 0.61 compared to observations and is statistically significant at the 0.01 level. Similarly, the forecasts with leading times of 48 h and 72 h are also sufficiently accurate, according to the correlation coefficients. In summer, however, forecasting is less accurate, although the correlation coefficient of the 24 h leading forecast is 0.59, which is still statistically significant at the 0.01 level. The forecasting of wind speed in Nantong was also examined with observed data and was determined to be more accurate than that in Rudong in both winter and in summer. For a larger domain, the performance of wind forecasting is also analyzed. Results show that the performance of the WRF model is better for ocean than for land and is better in winter than in summer. In general, the forecasted wind speed is greater than the actual wind speed and the performance of the WRF model foresting differs significantly according to location and season. Essentially, short-term wind forecasting is valuable and is worthy of further investigation.
- Wind power forecasting /
- Wind speed forecasting /
- WRF model

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参考文献(28)

[1]	Bailey B M, Brower J Z. 1999. Short-term wind forecasting: development and application of a mesoscale model [C]. // European Wind Energy Conference. Nice. France. 1062-1065.
[2]	Bessa R J, Miranda V, Botterud A, et al. 2011. ‘Good’ or ‘Bad’ wind power forecasts: A relative concept [J]. Wind Energy, 14 (5): 625-636.
[3]	Costa A, Crespo A, Navarro J, et al. 2008. A review on the young history of the wind power short-term prediction [J]. Renewable and Sustainable Energy Reviews, 12 (6): 1725-1744.
[4]	高荣珍, 杨育强, 孙桂平. 2007. 基于MM5的青岛近海风速精细化预报[J]. 海洋湖沼通报, 29 (4): 30-36. Gao Rongzhen, Yang Yuqiang, Sun Guiping. 2007. Study on refined wind speed forecast for Qingdao offshore zones [J]. Transactions of Oceanology and Limnology (in Chinese), 29 (4): 30-36.
[5]	Giebel G, Badger J, Perez I M, et al. 2006. Short-term forecasting using advanced physical modelling-the results of the ANEMOS project [C]. // European Wind Energy Conference (EWEC). Athens, Greece.
[6]	Giebel G, Brownsword R, Kariniotakis G, et al. 2011. The State-of-the-art in short-term prediction of wind power: A literature overview [R]. ANEMOS. plus.
[7]	谷兴凯, 范高锋, 王晓蓉, 等. 2007. 风电功率预测技术综述 [J]. 电网技术, 31 (增刊2): 335-338. Gu Xingkai, Fan Gaofeng, Wang Xiaorong, et al. 2007. Summarization of wind power prediction technology [J]. Power System Technology (in Chinese), 31 (Supplement 2): 335-338.
[8]	贺志明, 吴琼, 陈建萍. 2010. 沙岭风场风速预报试验分析 [J]. 资源科学, 32 (4): 656-662. He Zhiming, Wu Qiong, Chen Jianping. 2010. An experimental analysis of wind speed prediction in Shaling wind field [J]. Resources Science (in Chinese), 32 (4): 656-662.
[9]	Jursa R, Rohrig K. 2008. Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models [J]. International Journal of Forecasting, 24 (4): 694-709.
[10]	Kalnay E, Kanamitsu M, Kistler R, et al. 1996. The Ncep/NCAR 40-year reanalysis project [J]. Bull. Amer. Meteor. Soc., 77 (3): 437-471.
[11]	Kariniotakis G, Halliday J, Brownsword R, et al. 2006. Next generation short-term forecasting of wind power-overview of the ANEMOS project [C]. // European Wind Energy Conference, EWEC 2006. Athens, Greece.
[12]	Lange M, Focken U, Heinemann D. 2002. Previento-regional wind power prediction with risk control [C]. // Proceedings of the World Wind Energy Conference. Berlin.
[13]	Lei M, Luan S Y, Jiang C W, et al. 2009. A review on the forecasting of wind speed and generated power [J]. Renewable and Sustainable Energy Reviews, 13 (4): 915-920.
[14]	凌铁军, 张蕴斐, 杨学联, 等. 2004. 中尺度数值模式 (MM5) 在海面风场预报中的应用 [J]. 海洋预报, 21 (4): 1-9. Lin Tiejun, Zhang Yunfei, Yang Xuelian, et al. 2004. The application of MM5 model to predict sea surface wind field[J]. Marine Forecasts (in Chinese), 21 (4): 1-9.
[15]	Marti I, Kariniotakis G, Pinson P, et al. 2006. Evaluation of advanced wind power forecasting models-results of the ANEMOS project [C]. // European Wind Energy Conference, EWEC 2006. Athens, Greece.
[16]	Michalakes J, Dudhia J, Gill D, et al. 2005. The Weather Research and Forecast (WRF) model: software architecture and performance [R]. Proceedings of the Eleventh ECMWF Workshop on the Use of High Performance Computing in Meteorology. W. Zwieflhofer and G. Mozdzynski: 156-168
[17]	Nielsen T S, Madsen H. 2000. WPPT—a tool for wind power prediction[C]. // Procedings of Wind Power for the 21st Century. Kassel, Germany.
[18]	Nielsen T S, Madsen H, Nielsen H A, et al. 2006. Short-term wind power forecasting using advanced statistical methods [C]. // The European Wind Energy Conference, EWEC. Athens, Greece.
[19]	潘长明, 叶卓文. 1994. 利用GMDH方法作风速预报 [J]. 气象, 20 (5): 33-35. Pan Changming, Ye Zhuowen. 1994. GMDH for forecasting wind-velocity [J]. Meteorological Monthly (in Chinese), 20 (5): 33-35.
[20]	Pinson P, Chevallier C, Kariniotakis G. 2004. Optimizing benefits from wind power participation in electricity market using advanced tools for wind power forecasting and uncertainty Assessment [C]. // Proc. of the 2004 European Wind Energy Association Conference, EWEC’04. London, United Kingdom.
[21]	Pinson P, Siebert N, Kariniotakis G. 2003. Forecasting of regional wind generation by a dynamic fuzzy-neural networks based upscaling approach [C]. // European Wind energy and Conference. Madrid: Spain.
[22]	戚双斌, 王维庆, 张新燕. 2009. 基于支持向量机的风速与风功率预测方法研究 [J]. 华东电力, 37 (9): 1600-1603. Qi Shuangbin, Wang Weiqing, Zhang Xinyan. 2009. Wind speed and wind power prediction based on SVM [J]. East China Electric Power (in Chinese), 37 (9): 1600-1603.
[23]	戚双斌, 王维庆, 张新燕, 等. 2010. 基于SVM的风速风功率预测模型[J]. 可再生能源, 28 (4): 25-32. Qi Shuangbin, Wang Weiqing, Zhang Xinyan, et al. 2010. Model building for wind speed and wind power prediction based on SVM [J]. Renewable Energy (in Chinese), 28 (4): 25-32.
[24]	Ramirez-Rosado I J, Fernandez-Jimenez L A, Monteiro C, et al. 2009. Comparison of two new short-term wind-power forecasting systems [J]. Renewable Energy, 34 (7): 1848-1854.
[25]	Sideratos G, Hatziargyriou N D. 2007. An advanced statistical method for wind power forecasting [J]. IEEE Transactions on Power Systems, 22 (1): 258-265.
[26]	Skamarock W C, Klemp J B, Dudhia J, et al. 2008. A description of the advanced research WRF Version 3 [R]. NCAR Technical Note. Boulder, Colorado.
[27]	孙川永, 陶树旺, 罗勇, 等. 2009. 高分辨率中尺度数值模式在风电场风速预报中的应用 [J]. 太阳能学报, 30 (8): 1097-1099. Sun Chuanyong, Tao Shuwang, Luo Yong, et al. 2009. The application of high resolution mesoscale model in wind speed forecasting in wind farm [J]. Acta Energiae Solaris Sinica (in Chinese), 30 (8): 1097-1099.
[28]	涂小萍, 赵声蓉, 曾晓青, 等. 2008. KNN方法在11～3月中国近海测站日最大风速预报中的应用 [J]. 气象, 34 (6): 67-73. Tu Xiaoping, Zhao Shengrong, Zeng Xiaoqing, et al. 2008. Application of an updated KNN method to daily maximum wind forecast for coastal weather station from November to March [J]. Meteorological Monthly (in Chinese), 34 (6): 67-73.