Learning from ChatGPT: A Transformer-Based Model for Wind Power Forecasting DOI
Xiaoran Dai, Shuai Liu, Wenshan Hu

et al.

2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Journal Year: 2023, Volume and Issue: unknown, P. 1 - 6

Published: June 6, 2023

Wind power forecasting is a crucial aspect of re-newable energy production, as it helps to optimize output and ensure grid stability. In recent years, Transformer-based language models such ChatGPT have been successfully used in natural processing tasks, but their application wind remains largely unexplored. this article, we propose using Transformer model, the core ChatGPT, improve accuracy forecasting. Using self-attention mechanism, developed model can capture complex temporal relationships large-scale time series data. Furthermore, proposed method evaluated on test set various performance metrics. Results show that our outperforms traditional models, achieving higher accuracy. Our findings suggest significant potential for improving ultimately contributing more sustainable future.

Language: Английский

High and low frequency wind power prediction based on Transformer and BiGRU-Attention DOI
Shuangxin Wang, Jiarong Shi, Wei Yang

et al.

Energy, Journal Year: 2023, Volume and Issue: 288, P. 129753 - 129753

Published: Dec. 1, 2023

Language: Английский

Citations

41

A novel DWTimesNet-based short-term multi-step wind power forecasting model using feature selection and auto-tuning methods DOI
Chu Zhang, Yuhan Wang,

Yongyan Fu

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 301, P. 118045 - 118045

Published: Jan. 5, 2024

Language: Английский

Citations

23

Wind Power Forecasting in the presence of data scarcity: A very short-term conditional probabilistic modeling framework DOI
Sen Wang, Wenjie Zhang, Yonghui Sun

et al.

Energy, Journal Year: 2024, Volume and Issue: 291, P. 130305 - 130305

Published: Jan. 12, 2024

Language: Английский

Citations

18

Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research DOI Open Access
Han Peng, Songyin Li, Linjian Shangguan

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(10), P. 8333 - 8333

Published: May 20, 2023

Power generation from wind farms is growing rapidly around the world. In past decade, energy has played an important role in contributing to sustainable development. However, turbines are extremely susceptible component damage under complex environments and over long-term operational cycles, which directly affects their maintenance, reliability, operating costs. It crucial realize efficient early warning of turbine failure avoid equipment breakdown, prolong service life turbines, maximize revenue efficiency power projects. For this purpose, used as research object. Firstly, paper outlines main components mechanisms analyzes causes failure. Secondly, a brief analysis cost projects based on presented. Thirdly, current key technologies for intelligent operation maintenance (O&M) industry discussed, decision support systems, fault diagnosis models, life-cycle costs Finally, challenges future development directions summarized.

Language: Английский

Citations

30

Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy DOI
Xiaodi Wang, Hao Yan,

Wendong Yang

et al.

Energy, Journal Year: 2024, Volume and Issue: 297, P. 131142 - 131142

Published: April 3, 2024

Language: Английский

Citations

13

Carbon emission price point-interval forecasting based on multivariate variational mode decomposition and attention-LSTM model DOI
Liling Zeng, Huanling Hu, Huajun Tang

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 157, P. 111543 - 111543

Published: March 29, 2024

Language: Английский

Citations

11

Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approach DOI
Rongquan Zhang, Siqi Bu, Gangqiang Li

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 367, P. 123352 - 123352

Published: May 9, 2024

Language: Английский

Citations

8

Hybrid Transformer Model with Liquid Neural Networks and Learnable Encodings for Buildings’ Energy Forecasting DOI Creative Commons

Antonesi Gabriel,

Tudor Cioara, Ionuț Anghel

et al.

Energy and AI, Journal Year: 2025, Volume and Issue: unknown, P. 100489 - 100489

Published: Feb. 1, 2025

Language: Английский

Citations

1

Electrical load forecasting based on variable T-distribution and dual attention mechanism DOI
Jianguo Wang,

Lincheng Han,

Xiuyu Zhang

et al.

Energy, Journal Year: 2023, Volume and Issue: 283, P. 128569 - 128569

Published: July 28, 2023

Language: Английский

Citations

21

Ultra-short-term wind power forecasting based on personalized robust federated learning with spatial collaboration DOI
Yongning Zhao, Shiji Pan, Yuan Zhao

et al.

Energy, Journal Year: 2023, Volume and Issue: 288, P. 129847 - 129847

Published: Dec. 1, 2023

Language: Английский

Citations

19