Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer DOI Creative Commons
Mingxiang Li, Tianyi Zhang,

Haizhu Yang

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(20), P. 5181 - 5181

Published: Oct. 17, 2024

In order to solve the problem of complex coupling characteristics between multivariate load sequences and difficulty in accurate multiple forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission sources, this paper, TCN-FECAM-Informer model is proposed. First, maximum information coefficient (MIC) used correlate loads with weather factors filter appropriate features. Then, effective screened features extracted frequency sequence constructed using frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, processed feature are sent Informer forecasting. Experiments conducted measured data from IRES Arizona State University, experimental results show that TCN FECAM can greatly improve prediction accuracy and, at same time, demonstrate superiority network, dominated by attentional mechanism, compared recurrent neural networks prediction.

Language: Английский

Pulsar’s Application in Energy Systems: Review of Current Status, Challenges, and Opportunities DOI Creative Commons
Yongxin Zhang, Yuru Wu, Yu Liu

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(4), P. 828 - 828

Published: Feb. 11, 2025

To accelerate progress toward the realization of advanced energy systems, this review explores potential pulsar technology to create a more stable, economical, and environmentally friendly infrastructure. Pulsars, with their precise reliable timing characteristics, have emerged as promising tool for enhancing systems. This begins by examining development history technology, shedding light on its evolution milestones achieved. It then provides comprehensive summary current state research, highlighting recent advancements breakthroughs in field. also transformative applications including improved grid stability, synchronization, efficient storage management. However, implementing pulsar-related technologies presents significant technical, economic, operational challenges. examines these hurdles proposes strategies overcome them, emphasizing need innovation, interdisciplinary collaboration, supportive policies fully integrate into sustainable

Language: Английский

Citations

1
Optimization of thermal resistance and thermal deformation in high heat-load zone of blast furnace cooling staves DOI
Haifeng Chen, Yuling Zhai, Hao Huang

et al.

Applied Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 126292 - 126292

Published: March 1, 2025

Language: Английский

Citations

0
Dynamic Response Characteristics of Multi-Generation System Integrated with Gas and Heat Storage DOI
Xiaomeng Wang, Liqiang Duan, Nan Zheng

et al.

Journal of Thermal Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

Language: Английский

Citations

0
Scheduling optimization of park integrated energy system with a flywheel-based hybrid energy storage system and thermal power deep peak shaving DOI
Chong Gao, Ran Zhang,

Qinliang Tan

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 120, P. 116363 - 116363

Published: April 3, 2025

Language: Английский

Citations

0
Scheduling strategy for an electricity-heat-gas hybrid energy storage microgrid system considering novel combined heat and power units DOI
Nan Chen,

Junheng Gao,

Lihui Gao

et al.

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 4719 - 4733

Published: April 19, 2025

Language: Английский

Citations

0
Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer DOI Creative Commons
Mingxiang Li, Tianyi Zhang,

Haizhu Yang

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(20), P. 5181 - 5181

Published: Oct. 17, 2024

In order to solve the problem of complex coupling characteristics between multivariate load sequences and difficulty in accurate multiple forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission sources, this paper, TCN-FECAM-Informer model is proposed. First, maximum information coefficient (MIC) used correlate loads with weather factors filter appropriate features. Then, effective screened features extracted frequency sequence constructed using frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, processed feature are sent Informer forecasting. Experiments conducted measured data from IRES Arizona State University, experimental results show that TCN FECAM can greatly improve prediction accuracy and, at same time, demonstrate superiority network, dominated by attentional mechanism, compared recurrent neural networks prediction.

Language: Английский

Citations

1