Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power systems. However, the volatility and intermittence of wind power pose uncertainties to traditional point prediction, resulting in an increased risk of power system operation. To repre...

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Bibliographic Details
Main Authors: Wenlong Liao, Shouxiang Wang, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Zhe Yang, Kuangpu Liu
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:Journal of Modern Power Systems and Clean Energy
Subjects:
Wind power
graph neural network (GNN)
bidirectional long short-term memory (Bi-LSTM)
prediction interval
Bootstrap technique
Online Access:https://ieeexplore.ieee.org/document/10026203/

Internet

https://ieeexplore.ieee.org/document/10026203/

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