中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
WGformer: A Weibull-Gaussian Informer based model for wind speed prediction

文献类型：期刊论文

作者Shi, Ziyi7; Li, Jia6; Jiang, Zheyuan4,5; Li, Huang3; Yu, Chengqing2; Mi, Xiwei1
刊名ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
出版日期2024-05-01
卷号131页码:20
关键词Wind speed forecasting Weibull-Gaussian transform Informer Kernel mean square error loss Deep learning
ISSN号0952-1976
DOI10.1016/j.engappai.2024.107891
英文摘要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.
资助项目National Natural Science Foundation of China[52102471]
WOS研究方向Automation & Control Systems ; Computer Science ; Engineering
语种英语
WOS记录号WOS:001168517600001
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/38830]  
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.Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
3.Zhejiang Univ, Inst Ind Intelligence & Syst Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China
4.Zhejiang Univ, Polytech Inst, Hangzhou 310058, Peoples R China
5.Zhejiang Univ, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China
6.Southeast Univ, Sch Transportat, Nanjing 211189, Peoples R China
7.Zhejiang Univ, Inst Intelligent Transportat Syst, Coll Civil Engn & Architecture, Hangzhou 310058, 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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。