一种基于花授粉算法的WRF风速集合预报新模型
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摘要
基于不同水平分辨率和边界层参数化方案的集合预报思路,应用花授粉算法与不限制负值的约束理论(FPA-NNCT)进行权重平均,提出一种新的风速集合预报模型(FPA-NNCT-WRF-E).利用山东省代表山地和海滨下垫面的2个风电场风速实测数据,将新模型与传统算术集合模型(M-WRF-E)以及FPA模型(FPA-WRF-E)的风速预报结果进行对比评估.结果表明:FPA-NNCT-WRF-E预报明显优于M-WRF-E和FPA-WRF-E的风速预报,与M-WRF-E相比, FPA-WRF-E将风速平均绝对误差(MAE)减小了20%以上,而新模型FPA-NNCT-WRF-E将MAE减小了38%以上.预报的准确性得到了提高.
Considering multiple resolutions and boundary layer parameterizations, a novel ensemble forecast model(FPA-NNCT-WRF-E) is proposed in this paper, based on the weather research and forecasting model(WRF model), flower pollination algorithm(FPA) and no negative constraint theory(NNCT). And 10-day wind observations from two wind farms of different underlying surface are used to validate the effectiveness of this new model. The results show that FPA-NNCT-WRF-E is superior to M-WRF-E and FPA-WRF-E. Compared with M-WRF-E, FPA-WRF-E has reduced the mean absolute error(MAE) by more than 20%, while FPA-NNCT-WRF-E has reduced MAE by more than 38%.
Considering multiple resolutions and boundary layer parameterizations, a novel ensemble forecast model(FPA-NNCT-WRF-E) is proposed in this paper, based on the weather research and forecasting model(WRF model), flower pollination algorithm(FPA) and no negative constraint theory(NNCT). And 10-day wind observations from two wind farms of different underlying surface are used to validate the effectiveness of this new model. The results show that FPA-NNCT-WRF-E is superior to M-WRF-E and FPA-WRF-E. Compared with M-WRF-E, FPA-WRF-E has reduced the mean absolute error(MAE) by more than 20%, while FPA-NNCT-WRF-E has reduced MAE by more than 38%.
引文
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[2] 陈挚秋,王建力,杨平恒.2014年重庆市大气污染物浓度变化特征及其气象条件的关系 [J].西南大学学报(自然科学版),2016,38(10):147-153.
[3] 林万涛,王建州,张文煜,等.基于数值模拟和统计分析及智能优化的风速预报系统 [J].气候与环境研究,2012,17(5):646-658.
[4] MA L,LUAN S Y,JIANG C W,et al.A Review on the Forecasting of Wind Speed and Generated Power [J].Renewable & Sustainable Energy Reviews,2009,13(4):915-920.
[5] TORRES L J,GARCIA A,BLAS D M,et al.Forecast of Hourly Average Wind Speed with ARMA Models in Navarre (Spain) [J].Solar Energy,2005,79(1):65-77.
[6] 孔令彬,赵艳茹,王聚杰,等.基于支持向量机风速订正方法的研究 [J].西南大学学报(自然科学版),2014,36(5):194-200.
[7] RAMASAMY P,CHANDEL S S,YADAV K A.Wind Speed Prediction in the Mountainous Region of India Using an Artificial Neural Network Model [J].Renewable Energy,2015,80:338-347.
[8] JIMENEZ B,DURANTE F,LANGE B.Offshore Wind Resource Assessment with WAsP and MM5:Comparative Study for the German Bight [J].Wind Energy,2010,10(2):121-134.
[9] MATTAR C,BORVARAN D.Offshore Wind Power Simulation by Using WRF in the Central Coast of Chile [J].Renewable Energy,2016,94:22-31.
[10] YAHYAI A,CHARABI Y,GASTLI A.Review of the Use of Numerical Weather Prediction (NWP) Model for Wind Energy Assessment [J].Renewable and Sustainable Energy Reviews,2010,14(9):3192-3198.
[11] WAGENBRENNER S N,FORTHOFER M J,LAMB K B,et al.Downscaling Surface Wind Predictions from Numerical Weather Prediction Models in Complex Terrain with WindNinja [J].Atmospheric Chemistry & Phyciscs,2016,16(8):5229-5241.
[12] ZHANG D L,ZHENG W Z.Diurnal Cycles of Surface Winds and Temperatures as Simulated by Five Boundary Layer Parameterizations [J].Journal of Applied Meteorology and Climatology,2004,43(1):157-169.
[13] IORIO P J,DUFFY B P,GOVINDASAMY B et al.Effect of Model Resolution and Subgrid-scale Physics on the Simulation of Precipitation in the Continental United States [J].Climate Dynamics,2004,23(3-4):243-258.
[14] DEPPE J A,GALLUS A W,TAKLE S E.A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height [J].Weather Forecasting,2013,28(1):212-228.
[15] BOSSAVY A,GIRARD R,KARINIOTAKIS G.Forecasting Ramps of Wind Power Production with Numerical Weather Prediction Ensembles [J].Wind Energy,2013,16(1):51-63.
[16] LIU Y Q,YAN J,HAN S,et al.An Optimized Short-term Wind Power Interval Prediction Method Considering NWP Accuracy [J].Chinese Science Bulletin,2014,59(11):1167-1175.
[17] DONG Q L,SUN Y H,LI P Z.A Novel Forecasting Model Based on a Hybrid Processing Strategy and an Optimized Local Linear Fuzzy Neural Network to Make Wind Power Forecasting:A Case Study of Wind Farms in China [J].Renewable Energy,2017,102:241-257.
[18] YANG X S.Flower Pollination Algorithm for Global Optimization [M].Berlin Heidelberg:Springer,2012:240-249.
[19] XIAO L,WANG J Z,DONG Y,et al.Combined Forecasting Models for Wind Energy Forecasting:A Case Study in China [J].Renewable and Sustainable Energy Reviews,2015,44:271-288.