Analog Bias Correction of Numerical Model on Wind Power Prediction

Xu Jingjing; Hu Fei; Xiao Ziniu; Li Jun

Vol.24, NO.6, 2013

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2013 > 24(6): 731-740

Xu Jingjing, Hu Fei, Xiao Ziniu, et al. Analog bias correction of numerical model on wind power prediction. J Appl Meteor Sci, 2013, 24(6): 731-740.

Citation:

Xu Jingjing, Hu Fei, Xiao Ziniu, et al. Analog bias correction of numerical model on wind power prediction. J Appl Meteor Sci, 2013, 24(6): 731-740.

Xu Jingjing, Hu Fei, Xiao Ziniu, et al. Analog bias correction of numerical model on wind power prediction. J Appl Meteor Sci, 2013, 24(6): 731-740.

Citation:

Xu Jingjing, Hu Fei, Xiao Ziniu, et al. Analog bias correction of numerical model on wind power prediction. J Appl Meteor Sci, 2013, 24(6): 731-740.

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Analog Bias Correction of Numerical Model on Wind Power Prediction

Xu Jingjing^1,2,
Hu Fei^{2
,
,},
Xiao Ziniu^3,4,
Li Jun^2,5

1.
International Center for Climate and Environment Science, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
2.
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
3.
China Meteorological Administration Training Center, Beijing 100081
4.
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
5.
State Key Laboratory of Offshore Wind Power Generation Technology and Detection, Xiangtan 411101

Received Date: 2012-09-20
Rev Recd Date: 2013-08-01
Publish Date: 2013-12-31

Abstract

Abstract

A new post-processing method is proposed to reduce numerical weather prediction's systematic and random errors. The method overcomes a difficulty of a post-processing algorithm inspired by Kalman filtering and a 7-day running-mean correction in dealing with sudden changes of the forecast error that could be caused by rapid weather transitions. The analog forecast for a given location and time is defined as a past prediction that matches selected features of the current forecast. The method is the weighted average of observations that verifies when the best analogs are valid. The method is tested for 70-m wind speed prediction from Weather Research and Forecasting (WRF) model, with observations from one wind farm sited at Yanchang, Shaanxi Province for 3 months.The analog bias correction method is able to produce skillful corrections of the raw forecasts, even with large day-to-day changes in forecast error, and thus the method can predict drastic changes in forecast error. Moreover, being a prediction based solely on observations, it results in an efficient downscaling procedure that eliminates representativeness discrepancies between observations and predictions.Also, it is able to reduce random errors, therefore improving the predictive skill of raw forecast. The correction method is much better, with average improvement of 9.3% and 9.8% measured by root mean square error (RMSE) and centered root mean square error (CRMSE), respectively. Meanwhile, the method shows a better pattern of correspondence between predictions and observations.Moreover, the correction method for middle wind speed (5—12 m·s^-1), which plays the most important role on wind power prediction, is much better, with average improvement of 12.3% and 21.7% measured by RMSE and CRMSE, respectively. Thus the analog bias correction method is very suitable for wind power prediction.The analog bias correction method is based purely on verifying observations of past predictions that are similar to the forecast (i.e., the analogs), which provide physically based insight about the atmospheric state, thus improving the predictive skill. And it also has the potential to be applied to other prediction systems and variables.
- wind power;
- bias correction;
- analog bias correction method;
- atmospheric boundary layer

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References(28)

Figures and Tables

Fig. 1 Improvement of the bias correction method relative to the raw forecast as a function of the day-to-day variation of forecast absolute error (a) and counts of the binned magnitude of the day-to-day variation of forecast absolute error (b)

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Fig. 2 Taylor diagram of the raw forecast and the corrected

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Fig. 3 Averaged bias as a function of centered root mean square error

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图 4 原始预报和订正结果统计量随预报时间的演变

(a) 均方根误差, (b) 平均绝对偏差, (c) 平均偏差, (d) 相关系数

Fig. 4 Temporal evolution of the metrics for the raw forecast and the bias correction method

(a) root mean square error, (b) mean absolute error, (c) bias, (d) correlation

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Fig. 5 Counts of the binned wind speed of the corrected, the observation and the raw forecast

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Fig. 6 Taylor diagram of the raw forecast and the corrected for wind speed sensitive to wind power prediction

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Fig. 7 Bias as a function of centered root mean square error for wind speed sensitive to wind power prediction

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Table 1 Sensitivity analysis for weight of pressure w_p and number of analog forecast N_a

统计量	原始预报	N_a=10				w_p=0.1
统计量	原始预报	w_p=1	w_p=0.5	w_p=0.1	w_p=0	N_a=7	N_a=15	N_a=21	N_a=28
平均偏差/(m·s^-1)	0.08	0.33	0.31	0.19	0.02	0.20	0.17	0.17	0.17
平均绝对偏差/(m·s^-1)	1.65	1.61	1.59	1.58	1.60	1.59	1.57	1.56	1.56
均方根误差/(m·s^-1)	2.15	2.03	2.01	1.98	2.00	2.00	1.97	1.95	1.95
中心均方根误差/(m·s^-1)	2.15	2.00	1.99	1.97	2.00	1.99	1.96	1.94	1.94
相关系数	0.63	0.63	0.63	0.63	0.61	0.62	0.63	0.64	0.64
秩相关系数	0.63	0.62	0.62	0.63	0.62	0.62	0.63	0.64	0.64

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Table 2 Statistics for evaluation of the raw forecast and the corrected

统计量	原始预报	订正结果
偏差的标准差/(m·s^-1)	2.15	1.94
平均偏差的标准差/(m·s^-1)	1.37	1.18
标准化偏差的标准差/(m·s^-1)	26.7	11.4

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Table 3 Statistics of the raw forecast and the corrected for wind speed sensitive to wind power prediction

统计量	原始预报	订正结果
平均偏差/(m·s^-1)	-0.74	-1.05
平均绝对偏差/(m·s^-1)	1.72	1.55
均方根误差/(m·s^-1)	2.20	1.93
中心均方根误差/(m·s^-1)	2.07	1.62

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References

[1]	Burton T. 风能技术. 武鑫, 译. 北京: 科学出版社, 2007.
[2]	胡非.湍流、间歇性与大气边界层.北京:科学出版社, 1995. http://www.cnki.com.cn/Article/CJFDTOTAL-SYQY201603027.htm
[3]	刘东海, 宋丽莉, 李国平, 等.强台风"黑格比"实测海上风电机组极端风况特征参数分析和讨论.热带气象学报, 2011, 27(3):317-326. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX201103004.htm
[4]	Lange M, Focken U.Physical Approach to Short-term Wind Power Prediction.Berlin:Springer, 2006.
[5]	杨秀媛, 肖洋, 陈树勇.风电场风速和发电功率预测研究.中国电机工程学报, 2005, 25(11):1-5. doi: 10.3321/j.issn:0258-8013.2005.11.001
[6]	Costa A, Crespo A, Navarro J, et al.A review on the young history of the wind power short-term prediction.Renewable & Sustainable Energy Reviews, 2008, 12:1725-1744.
[7]	邢旭煌, 朱蓉, 翟盘茂, 等.海南省及其近海风能资源的高分辨率数值模拟.热带气象学报, 2009, 25(4):421-426. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200904005.htm
[8]	Yim S H L, Fung J C H, Lau A K H, et al.Developing a high-resolution wind map for a complex terrain with a coupled MM5 CALMET system.J Geophys Res, 2007, 112:D05106, doi: 10.1029/2006JD007752.
[9]	穆海振, 徐家良, 柯晓新, 等.高分辨率数值模式在风能资源评估中的应用初探.应用气象学报, 2006, 17(2):152-159. doi: 10.11898/1001-7313.20060204
[10]	Kalnay E. 大气模式、资料同化和可预报性. 蒲朝霞, 杨福全, 译. 北京: 气象出版社, 2005: 115-118.
[11]	王在文, 郑柞芳, 陈敏, 等.支持向量机非线性回归方法的气象要素预报.应用气象学报, 2012, 23(5):562-570. doi: 10.11898/1001-7313.20120506
[12]	程兴宏, 陶树旺, 魏磊, 等.基于WRF模式和自适应偏最小二乘回归算法的风能预报试验研究.高原气象, 2012, 31(5):1461-1469. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201205031.htm
[13]	刘还珠, 赵声蓉, 陆志善, 等.国家气象中心气象要素的客观预报——MOS系统.应用气象学报, 2004, 15(2):181-191. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040223&flag=1
[14]	许建明, 徐祥德, 刘煜, 等.CMAQ-MOS区域空气质量统计修正模型预报途径研究.中国科学:D辑, 2005, 35(增刊Ⅰ):131-144. http://www.cnki.com.cn/Article/CJFDTOTAL-JDXK2005S1013.htm
[15]	Kalman R E.A new approach to linear filtering and prediction problems.J Basic Eng, 1960, 82:35-45. doi: 10.1115/1.3662552
[16]	Muller M D.Effects of model resolution and statistical postprocessing on shelter temperature and wind forecasts.J Appl Meteor, 2011, 50:1627-1636. doi: 10.1175/2011JAMC2615.1
[17]	Crochet P.Adaptive Kalman filtering of 2-m temperature and 10-m wind-speed forecasts in Iceland.Meteor Appl, 2004, 11:173-187. doi: 10.1017/S1350482704001252
[18]	Louka P, Galanis G, Siebert N, et al.Improvemens in wind speed forecasts for wind power prediction purposes using Kalman filtering.J Wind Eng Ind Aerodyn, 2008, 96:2348-2362. doi: 10.1016/j.jweia.2008.03.013
[19]	Delle M L, Nipen T, Deng X, et al.Ozone ensemble forecasts:2.A Kalman-filter predictor bias correction.J Geophys Res, 2006, 111:D05308, doi: 10.1029/2005JD006311.
[20]	陈豫英, 刘还珠, 陈楠, 等.基于聚类天气分型的KNN方法在风预报中的应用.应用气象学报, 2008, 19(5):564-572. doi: 10.11898/1001-7313.20080507
[21]	邵明轩, 刘还珠, 窦以文.用非参数估计技术预报风的研究.应用气象学报, 2006, 17(增刊):125-129. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2006S1017.htm
[22]	任宏利, 丑纪范.统计-动力相结合的相似误差订正法.气象学报, 2005, 63(6):988-993. doi: 10.11676/qxxb2005.094
[23]	Gao L, Ren H, Li J, et al.Analogue correction method of errors and its application to numerical weather prediction.Chin Phys, 2006, 15:882-889. doi: 10.1088/1009-1963/15/4/038
[24]	Xavier P K, Goswami B N.An analog method for real-time forecasting of summer monsoon subseasonal variability.Mon Wea Rev, 2007, 135:4149-4160. doi: 10.1175/2007MWR1854.1
[25]	Hamill T M, Whitaker J S.Probabilistic quantitative precipitation forecasts based on reforecast analogs:Theory and application.Mon Wea Rev, 2006, 134:3209-3229. doi: 10.1175/MWR3237.1
[26]	Van den Dool H.Empirical Methods in Short-term Climate Prediction.Oxford:Oxford University Press, 2007:1-240.
[27]	Delle M L, Nipen T, Liu Y, et al.Kalman filter and analog schemes to post-process numerical weather predictions.Mon Wea Rev, 2011, 139:3554-3570. doi: 10.1175/2011MWR3653.1
[28]	李军. 复杂地形的风能资源精细化评估和风电功率预报. 北京: 中国科学院研究生院, 2012: 99-101.