Ultra-short-term Prediction of Wind Farm Cluster Power Based on Embedded Graph Structure Learning with Spatiotemporal Information Gain

IF 8.6 1区工程技术 Q1 ENERGY & FUELS

IEEE Transactions on Sustainable Energy Pub Date : 2024-09-06 DOI:10.1109/tste.2024.3455759

Mao Yang, Yunfeng Guo, Fulin Fan

引用次数: 0

查看原文本刊更多论文

基于时空信息增益的嵌入式图结构学习的风电场集群功率超短期预测

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Sustainable Energy ENERGY & FUELS-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

21.40

自引率

5.70%

发文量

215

审稿时长

5 months

期刊介绍： The IEEE Transactions on Sustainable Energy serves as a pivotal platform for sharing groundbreaking research findings on sustainable energy systems, with a focus on their seamless integration into power transmission and/or distribution grids. The journal showcases original research spanning the design, implementation, grid-integration, and control of sustainable energy technologies and systems. Additionally, the Transactions warmly welcomes manuscripts addressing the design, implementation, and evaluation of power systems influenced by sustainable energy systems and devices.