摘要
风速预测是风电场风功率预测的基础,其准确度严重影响着风电场的运行效率。为了提高短期风速预测的准确性,本研究采用了WRF中尺度数值模式,对我国东部沿海某风电场的风速进行预报。在此基础上,利用极限学习机算法(ELM)对WRF模式预报的风速进一步订正。实验结果表明,WRF模式对风速、风向等气象要素有着较好的回报效果,利用ELM算法对WRF模式预报风速进行订正后,预报风速的误差进一步减小,相对均方根误差和相对平均绝对误差降低了20%～30%。与其他的智能算法(BP神经网络、SVM算法)对比分析后得出,ELM算法对WRF模式预报风速具有较好的订正效果,能够有效提高风速预报准确率。
Wind speed forecasting is the basis of wind power forecasting,and its accuracy affects the efficiency of the wind farm seriously.In order to improve the accuracy of short-term wind speed forecasting,the WRF mesoscale numerical model was used to predict the wind speed of a wind farm in the eastern coasts of China.Besides,the Extreme Learning Machine（ELM） algorithm was used for further correction.The results show that,the WRF model has a better effect on the wind speed,wind direction and other meteorological elements.After correcting the wind speed forecasting of the WRF model,with the ELM algorithm,the error of wind speed forecasting becomes smaller,and the relative root mean square error and relative mean absolute error are reduced by 20%—30%.Thus,the ELM algorithm is qualified to have better correction capability for the wind speed of WRF model forecasting compared with other intelligent algorithms（BP neural network,SVM algorithm）,and can improve the accuracy of wind speed forecasting effectively.
出处
《气象》
CSCD
北大核心
2016年第4期466-471,共6页
Meteorological Monthly
基金
江苏省高校优势学科建设工程资助项目
江苏省六大人才高峰项目(WLW-021)
江苏省研究生创新工程省立项目(SJLX_0386)
公益性行业(气象)科研专项(GYHY201106040)共同资助
关键词
WRF模式
ELM算法
误差订正
预报效果
WRF model
Extreme Learning Machine（ELM） algorithm
bias correction
prediction effect
