Wind-speed forecasting model based on DBN-Elman combined with improved PSO-HHT

IF 1.9 Q4 ENERGY & FUELS Global Energy Interconnection Pub Date : 2023-10-01 DOI:10.1016/j.gloei.2023.10.002
Wei Liu , Feifei Xue , Yansong Gao , Wumaier Tuerxun , Jing Sun , Yi Hu , Hongliang Yuan
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引用次数: 0

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.

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基于DBN-Elman结合改进PSO-HHT的风速预报模型
随机和波动的风速使风电输出难以稳定,这使风电场控制系统的执行复杂化并增加了响应频率。在本研究中,将深度置信网络、Elman神经网络和使用改进的粒子群优化算法修改的Hilbert-Huang变换相结合,开发了一种新的风电场短期风速预测模型。实验结果表明,所提出的深度神经网络的预测结果优于浅层神经网络。尽管该模型的复杂性很高,但风速预测的准确性和稳定性也很高。该模型有效地提高了风电场短期风速预测的准确性。
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来源期刊
Global Energy Interconnection
Global Energy Interconnection Engineering-Automotive Engineering
CiteScore
5.70
自引率
0.00%
发文量
985
审稿时长
15 weeks
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