电力系统保护与控制2017，Vol.45Issue(22)：48-54,7.DOI:10.7667/PSPC161827
基于WRF模式和PSO-LSSVM的风电场短期风速订正
Modification technology research of short-term wind speed in wind farm based on WRF model and PSO-LSSVM method
摘要
Abstract
Wind speed forecasting is the base and precondition of wind power prediction of wind farm. The Numerical Weather Prediction (WRF) model is used to predict wind speed. In order to improve the accuracy of WRF model, the Least Square Support Vector Machine (LSSVM) is used to correct the wind speed of the output of the WRF model. At the same time, in order to improve the accuracy of the LSSVM model and reduce the complexity of the fitting process, Particle Swarm Algorithm (PSO) is used to optimize the parameters. Experimental results show that the LSSVM can further reduce the error of WRF model in predicting wind speed sequence, and the relative root mean square error and the relative to the average absolute error are reduced by 5%~10%, the RMS error decreased by 0.5 m/s. Compared with without optimized LSSVM and ELM, PSO-LSSVM has a better correction effect in wind speed predicting by WRF to improve the accuracy of wind speed forecasting.关键词
风力发电/风速订正/WRF模式/PSO-LSSVM/预测效果Key words
wind power/wind speed correction/WRF model/PSO-LSSVM/forecast effect引用本文复制引用
叶小岭,顾荣,邓华,陈浩,杨星..基于WRF模式和PSO-LSSVM的风电场短期风速订正[J].电力系统保护与控制,2017,45(22):48-54,7.基金项目
国家自然科学基金项目(41675156) （41675156）
国家公益性行业(气象)科研专项(GYHY20110604) （气象）
江苏省六大人才高峰项目(WLW-021)资助 （WLW-021）
江苏省研究生创新工程省立项目(SJZZ16_0155) This work is supported by National Natural Science Foundation of China (No. 41675156). （SJZZ16_0155）
