WGformer: A Weibull-Gaussian Informer based model for wind speed prediction

doi:10.1016/j.engappai.2024.107891

	WGformer: A Weibull-Gaussian Informer based model for wind speed prediction
	Shi, Ziyi 1; Li, Jia 2; Jiang, Zheyuan 3,4; Li, Huang 5; Yu, Chengqing 6; Mi, Xiwei 7
	2024-05-01
发表期刊	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
ISSN	0952-1976
卷号	131 页码:20
摘要	Accurate wind speed forecasting can improve energy management efficiency and promote the use of renewable energy. However, the inherent nonlinearity and fluctuation of wind speed make prediction challenging. To address these issues, we design an efficient Informer-based model, with improved calculation speed, forecasting accuracy and generalization ability. The proposed model in this paper reasonably integrates the WeibullGaussian transform, Informer and kernel mean square error loss and addresses the combination of various components. The Weibull-Gaussian transform is used as the data preprocessing module, which can remove nonGaussian characteristics from the original data, and thus achieve noise reduction. The Informer is used as the main predictor, which can efficiently output accurate forecasting results based on an encoder-decoder architecture and self-attention mechanism. The kernel mean square error loss function, which shows strong robustness to outliers, is used to evaluate the nonlinearity of errors in reproducing kernel Hilbert space. To evaluate the performance of the proposed model, it is compared with several widely used models and state-of-the-art models. The experimental results indicate that the proposed model weakens the effect of outliers, yields high forecasting accuracy with mean square error = 0.35, and outperforms the baselines up to 8.5% on three datasets.
关键词	Wind speed forecasting Weibull-Gaussian transform Informer Kernel mean square error loss Deep learning
DOI	10.1016/j.engappai.2024.107891
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[52102471]
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
WOS记录号	WOS:001168517600001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38831
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Mi, Xiwei
作者单位	1.Zhejiang Univ, Inst Intelligent Transportat Syst, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China 2.Southeast Univ, Sch Transportat, Nanjing 211189, Peoples R China 3.Zhejiang Univ, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China 4.Zhejiang Univ, Polytech Inst, Hangzhou 310058, Peoples R China 5.Zhejiang Univ, Inst Ind Intelligence & Syst Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China 6.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 7.Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China
推荐引用方式 GB/T 7714	Shi, Ziyi,Li, Jia,Jiang, Zheyuan,et al. WGformer: A Weibull-Gaussian Informer based model for wind speed prediction[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2024,131:20.
APA	Shi, Ziyi,Li, Jia,Jiang, Zheyuan,Li, Huang,Yu, Chengqing,&Mi, Xiwei.(2024).WGformer: A Weibull-Gaussian Informer based model for wind speed prediction.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,131,20.
MLA	Shi, Ziyi,et al."WGformer: A Weibull-Gaussian Informer based model for wind speed prediction".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 131(2024):20.