基于IAOA-VMD-LSTM的超短期风电功率预测  

ULTRA SHORT TREM WIND POWER PREDICTION BASED ON IAOA-VMD-LSTM

作　　者：肖烈禧 张玉[1,2] 周辉 赵冠皓 Xiao Liexi;Zhang Yu;Zhou Hui;Zhao Guanhao(College of Mechanical and Control Engineering,Guilin University of Technology,Guilin 541006,China;Guangxi Key Laboratory of Building New Energy and Energy Saving,Guilin 541006,China)

机构地区：[1]桂林理工大学机械与控制工程学院,桂林541006 [2]广西建筑新能源与节能重点实验室,桂林541006

出　　处：《太阳能学报》2023年第11期239-246,共8页Acta Energiae Solaris Sinica

基　　金：广西建筑新能源与节能重点实验室开放研究基金项目(桂科能17-J-21-4)。

摘　　要：为了对风电功率进行精确预测,提出一种基于改进算术优化算法(IAOA)、变分模态分解(VMD)和长短期记忆网络(LSTM)的超短期风电功率预测模型(IAOA-VMD-LSTM)。利用IAOA对VMD的关键分解参数k和α进行优化,得到的各固有模态函数(IMF)具有周期性,能够提升LSTM的预测精度,同时利用IAOA对LSTM网络参数进行优化。通过对风电功率数据进行预测分析,结果表明IAOA-VMD-LSTM预测模型相比于其他模型的预测精度更高。In order to accurately predict wind power,an ultra-short-term wind power prediction model was proposed based on improved arithmetic optimization algorithm(IAOA),variational modal decomposition(VMD)and long short-term memory network(LSTM).The IAOA algorithm was used to optimize the key decomposition parameters k andαof VMD,and the inherent modal functions(IMF)obtained were periodic,which could improve the prediction accuracy of LSTM.Meanwhile,the IAOA algorithm was used to optimize the LSTM network parameters.Through the prediction analysis of wind power data,the results show that the IAOA-VMD-LSTM prediction model has higher prediction accuracy than other models.

关 键 词：风电功率预测 变分模态分解 长短期记忆网络 算术优化算法 

分 类 号：TM614[电气工程—电力系统及自动化]