|
|
|
| Wind Speed Prediction Based on EEMD Analysis and AR Modeling |
| HE Qun,ZHAO Wen-shuang,JIANG Guo-qian,XIE Ping |
| Institute of Electrical Engineering, Yanshan University, Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, China |
|
|
|
|
Abstract Aiming at the non-stationary and nonlinear of wind speed sequences ,an integrated method based on EEMD and AR modeling is proposed. The wind speed time series are firstly pretreated by EEMD and decomposed into a series of relatively smooth IMF components, highlighting the local characteristics of the original sequences. Then each IMF component is modeled and forecasted using AR modeling, thus reducing the difficulty of modeling and forecast costs. Eventually, the prediction results of each component are taken for integration by the least square method to get the right values. A set of wind speed data from some wind farm are verified and the results show that compared with the single AR modeling prediction and forecast based on EMD and AR integration, the proposed method can effectively improve the prediction accuracy.
|
|
|
|
|
|
［1］叶林,刘鹏.基于经验模态分解和支持向量机的短期风电功率组合预测模型［J］.中国电机工程学报,2011,31(31):102-108.
［2］刘兴杰,米增强,杨奇逊,等.基于经验模式分解和时间序列分析的风电场风速预测［J］.太阳能学报,2010,31(8):1037-1041.
［3］孙国强,卫志农,翟玮星.基于RVM与ARMA误差校正的短期风速预测［J］.电工技术学报,2012,27(8):187-193.
［4］夏冬,吴俊勇,贺电,等.一种新型的风电功率预测综合模型［J］.电工技术学报,2011,26(1):262-266.
［5］Huang N E, Shen Z, Long S, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series［C］//Proceedings of the Royal Society of London Series A, London, UK,1998.
［6］谢平,王欢,杜义浩,等.基于EMD和Wigner-Ville分布的机械故障诊断方法研究［J］.计量学报,2010,31(5):390-394.
［7］Wang T, Zhang M C, Yu Q H, et al. Comparing the applications of EMD and EEMD on time-frequency analysis of seismic signal ［J］. Journal of Applied Geophysics, 2012,83:29-34.
［8］Wu Z H, Huang N E. Ensembel Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method［J］. Advances in Adaptive Data Analysis, 2009,1(1):1-41.
［9］张淑清,董璇,翟欣沛,等.基于EEMD和混沌的信号特征提取方法及应用［J］.计量学报,2013,34(2):173-179.
［10］张烨,田雯,刘盛鹏,等.基于集合经验模式分解的火灾时间序列预测［J］.计算机工程, 2012,38(24):152-155.
［11］ 栗然,陈倩,徐宏锐.考虑相关因素的最小二乘支持向量机风速预测［J］.电力系统保护与控制,2010,38(21):146-151. |
|
|
|
2015, Vol. 36 
