全球能源互联网（英文）2023，Vol.6Issue(5)：530-541,12.DOI:10.1016/j.gloei.2023.10.002
基于DBN-Elman和改进PSO-HHT方法的风速预测模型
Wind-speed forecasting model based on DBN-Elman combined with improved PSO-HHT
摘要
Abstract
Random and fluctuating wind speeds make it difficult to stabilize the wind-power output,which complicates the execution of wind-farm control systems and increases the response frequency.In this study,a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network,the Elman neural network,and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm.The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks.Although the complexity of the model is high,the accuracy of wind-speed prediction and stability are also high.The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms.关键词
风速预测/DBN/Elman/HHT/组合神经网络Key words
Wind-speed forecasting/DBN/Elman/HT/Combined neural network引用本文复制引用
刘玮,薛飞飞,高岩松,吾买尔·吐尔逊,孙静,胡义,袁红亮..基于DBN-Elman和改进PSO-HHT方法的风速预测模型[J].全球能源互联网（英文）,2023,6(5):530-541,12.基金项目
This study was supported by the Research and Application of Key Technologies in the Design of Large Onshore Smart Wind Power Base(Grant No.XBY-ZDKJ-2020-05),the Scientific Research Project of the China Electric Power Construction Corporation:Research and Application of Key Technologies in the Design of an Onshore Smart Wind Power Base(Grant No.DJ-ZDXM-2020-52),the Danish Energy Agency(Grant No.64013-0405),the Fundamental Research Funds for the Central Universities(Grant No.B210201018),and the Jiangsu Province Policy Guidance Program(Grant No.BZ2021019). （Grant No.XBY-ZDKJ-2020-05）
