An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model-Reference-Cited by-同舟云学术

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An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model

Published:2024-04 Issue: Volume:293 Page:130751
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Lv Yunlong^ORCID,Hu Qin,Xu Hang,Lin Huiyao,Wu Yufan

Publisher

Elsevier BV

Reference62 articles.

1. A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks;Chengqing;Energy,2023

2. Forecasting short-term wind speed based on IEWT-LSSVM model optimized by bird swarm algorithm;Xiang;IEEE Access,2019

3. Short-term wind speed forecasting based on spatial-temporal graph transformer networks;Pan;Energy,2022

4. Prediction method of wind farm power generation capacity based on feature clustering and correlation analysis;Wang;Elec Power Syst Res,2022

5. Medium-term wind power forecasting based on multi-resolution multi-learner ensemble and adaptive model selection;Chen;Energy Convers Manag,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Interpretable multi-graph convolution network integrating spatio-temporal attention and dynamic combination for wind power forecasting;Expert Systems with Applications;2024-12

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