Adaptive Feature Selection and GCN With Optimal Graph Structure-Based Ultra-Short-Term Wind Farm Cluster Power Forecasting Method-Reference-Cited by-同舟云学术

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Adaptive Feature Selection and GCN With Optimal Graph Structure-Based Ultra-Short-Term Wind Farm Cluster Power Forecasting Method

Published:2024-01 Issue:1 Volume:60 Page:1804-1813
ISSN:0093-9994
Container-title:IEEE Transactions on Industry Applications
language:
Short-container-title:IEEE Trans. on Ind. Applicat.

Author:

Xu Honglai¹^ORCID,Zhang Yiran²,Zhen Zhao¹^ORCID,Xu Fei³,Wang Fei¹^ORCID

Affiliation:

1. Department of Electrical Engineering, North China Electric Power University, Baoding, China

2. School of International Education(BAODING), North China Electric Power University, Baoding, China

3. Department of Electrical Engineering, Tsinghua University, Beijing, China

Funder

National Key R&D Program of China

Multi-Timescale Forecast Technology for Large-Scale Wind/Photovoltaic Power Supply Capability

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/28/10410107/10269716.pdf?arnumber=10269716

Reference49 articles.

1. An ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling

2. Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach

3. Sky Image Prediction Model Based on Convolutional Auto-Encoder for Minutely Solar PV Power Forecasting

4. A Spatio-Temporal Analysis Approach for Short-Term Forecast of Wind Farm Generation

5. Hybrid model with secondary decomposition, randomforest algorithm, clustering analysis and long short memory network principal computing for short-term wind power forecasting on multiple scales

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. Ultra-Short-Term Wind Power Prediction Based on the ZS-DT-PatchTST Combined Model;Energies;2024-08-29

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