Enhancement of wind speed forecasting using optimized decomposition technique, entropy-based reconstruction, and evolutionary PatchTST DOI

Changwen Ma,

Chu Zhang,

Junhao Yao

et al.

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 333, P. 119819 - 119819

Published: April 23, 2025

Language: Английский

Environmental policy-driven electricity consumption prediction: A novel buffer-corrected Hausdorff fractional grey model informed by two-stage enhanced multi-objective optimization DOI
Yuansheng Qian, Zhijie Zhu, Xinsong Niu

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 377, P. 124540 - 124540

Published: Feb. 24, 2025

Language: Английский

Citations

0
A cross domain processing deep transfer learning network for rotating machinery fault diagnosis DOI
Bo Fu,

Li Xu,

Yi Quan

et al.

Measurement Science and Technology, Journal Year: 2025, Volume and Issue: 36(4), P. 046132 - 046132

Published: April 8, 2025

Abstract In the field of intelligent fault diagnosis mechanical equipment, existing cross-domain diagnostic models based on transfer learning (TL) do not utilise commonality information between two domains in data processing stage, which leads to loss transferable features that are essential for task. To address this issue, paper proposes a deep TL network model (CDPDTLN), consists (CDP) module, feature extraction module and domain-adaptive module. CDP adaptive multivariate variational modal decomposition algorithm is used process source target domain simultaneously, preserving common domains. realise work under various complex operating conditions, an improved multi-scale residual proposed extract domain-invariant features. combined distribution adaptation (CDDA) strategy align marginal conditional distributions CDDA strategy, weighted mean square discrepancy metric defined by combining maximum with enhance alignment confusion capabilities. multi-scenario experiments, accuracy CDPDTLN exceeds 95%. The results show can effectively retain learn features, significantly improving reliability robustness diagnosis.

Language: Английский

Citations

0
A TSFLinear model for wind power prediction with feature decomposition-clustering DOI
Huawei Mei, Qingyuan Zhu,

Cao Wangbin

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 123142 - 123142

Published: April 1, 2025

Language: Английский

Citations

0
Enhancement of wind speed forecasting using optimized decomposition technique, entropy-based reconstruction, and evolutionary PatchTST DOI

Changwen Ma,

Chu Zhang,

Junhao Yao

et al.

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 333, P. 119819 - 119819

Published: April 23, 2025

Language: Английский

Citations

0