Research on data augmentation and synthetic sample quantity uncertainty in few-shot wind power prediction based on the adaptive CRITIC-HLICRVFL method DOI

Shihao Song,

Anbo Meng, Liexi Xiao

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 123527 - 123527

Published: May 1, 2025

Language: Английский

A Multi-objective transfer learning framework for time series forecasting with Concept Echo State Networks DOI Creative Commons
Yingqin Zhu, Wen Yu, Xiaoou Li

et al.

Neural Networks, Journal Year: 2025, Volume and Issue: 186, P. 107272 - 107272

Published: Feb. 20, 2025

Language: Английский

Citations

1
Optimized echo state network for error compensation based on transfer learning DOI Creative Commons
Yingqin Zhu, Yue Liu,

Zhaozhao Zhang

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112935 - 112935

Published: March 1, 2025

Language: Английский

Citations

0
Cross-domain knowledge transfer in industrial process monitoring: A survey DOI
Zheng Chai, Chunhui Zhao, Biao Huang

et al.

Journal of Process Control, Journal Year: 2025, Volume and Issue: 149, P. 103408 - 103408

Published: March 23, 2025

Language: Английский

Citations

0
A Modularity-Enhanced Echo State Network for Nonlinear Wind Energy Predicting DOI Creative Commons

Shengqin Yue,

Zhili Zhao,

Taavi Lai

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(7), P. 1858 - 1858

Published: April 7, 2025

With the rapid growth of wind power generation, accurate energy prediction has emerged as a critical challenge, particularly due to highly nonlinear nature speed data. This paper proposes modularized Echo State Network (MESN) model improve forecasting. To enhance generalization, data is first decomposed into time series components, and Modes-cluster employed extract trend patterns pre-train ESN output layer. Furthermore, Turbines-cluster groups turbines based on their characteristics, enabling within same category share matrix for prediction. An integration module then introduced aggregate predicted results, while modular design ensures efficient task allocation across different modules. Comparative experiments with other neural network models demonstrate effectiveness proposed approach, showing that statistical RMSE parameter error reduced by an average factor 2.08 compared traditional models.

Language: Английский

Citations

0
Analysis of electricity and carbon coupling error in coal-fired power plants based on non-parametric kernel density estimation DOI Open Access
Delong Zhang, Jiaqi Li, Jiakun Wang

et al.

Journal of Physics Conference Series, Journal Year: 2025, Volume and Issue: 3000(1), P. 012010 - 012010

Published: April 1, 2025

Abstract With the increasingly severe global climate change problem, accurate monitoring and assessment of carbon emissions have become critical. However, measurement is often accompanied by uncertainty errors, statistical characteristics these errors require further investigation. This paper aims to explore error probability modeling electric coupling relationship quantify analyze associated developing a non-parametric kernel density estimation model. Analysis actual data from coal-fired power plants demonstrates that proposed model effectively fits between electricity generation emissions.

Language: Английский

Citations

0
Research on data augmentation and synthetic sample quantity uncertainty in few-shot wind power prediction based on the adaptive CRITIC-HLICRVFL method DOI

Shihao Song,

Anbo Meng, Liexi Xiao

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 123527 - 123527

Published: May 1, 2025

Language: Английский

Citations

0