返回
Wind Power Forecast Using Multilevel Adaptive Graph Convolution Neural Network
DOI:10.3390/en19010186.png)
摘要
En
Keyword:
wind power forecasting
spatiotemporal modeling
adaptive graph convolution networks
temporal transformer
attention mechanisms
renewable energy prediction
wind farm spatiotemporal correlation
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.2
论文数:
1.6W
被引数:
14.2W
机构
引用论文
A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting
APPLIED ENERGY
IF11
A method for ultra-short-term wind power forecasting of large-scale wind farms based on adaptive spatiotemporal graph convolution一种基于自适应时空图卷积的大规模风电场超短期风力发电预测方法
RENEWABLE ENERGY
IF9.1
Long-Term Wind Power Forecasting Using Tree-Based Learning Algorithms基于树形学习算法的长期风电功率预测
IEEE ACCESS
IF3.6
A Spatiotemporal Directed Graph Convolution Network for Ultra-Short-Term Wind Power Prediction用于超短期风电功率预测的时空有向图卷积网络

