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Article

Optimizing Steel Industry and Air Conditioning Clusters Using Coordination-Based Time-Series Fusion Transformer

1
School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
2
Beijing Key Laboratory of Demand-Side Multi-Energy Complementary Optimization and Supply-Demand Interaction Technology, China Electric Power Research Institute, Beijing 100192, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Processes 2025, 13(10), 3265; https://doi.org/10.3390/pr13103265 (registering DOI)
Submission received: 27 August 2025 / Revised: 5 October 2025 / Accepted: 10 October 2025 / Published: 13 October 2025

Abstract

The steel industry, a typical energy-intensive sector, experiences significant load power fluctuations, particularly during peak periods, posing challenges to power-grid stability. Traditional studies often overlook its unique production characteristics, limiting a comprehensive understanding of power fluctuations. Meanwhile, air conditioning (AC), as a flexible load, offers stable regulation with an aggregation effect. This study explores the potential for coordinated load dispatch between the steel industry and air conditioning clusters to enhance power system flexibility. A power characteristic model for steel loads was developed based on energy consumption patterns, while a physical ETP model aggregated air conditioning loads. To improve forecasting accuracy, a parallel LSTM-Transformer model predicts both steel and air conditioning loads. CEEMDAN-VMD decomposition reduces noise in steel-load data, and the QR algorithm computes confidence intervals for load responses. The study further examines interactions between electric-arc furnace control strategies and air conditioning demand response. Case studies using real-world data demonstrate that the proposed model enhances prediction accuracy, peak suppression, and variance reduction. These findings provide insights into steel industry power fluctuations and large-scale air conditioning load adjustments.
Keywords: demand response; CEEMDAN-VMD decomposition; air conditioning clusters; electric-arc furnace control; coordinated load dispatch demand response; CEEMDAN-VMD decomposition; air conditioning clusters; electric-arc furnace control; coordinated load dispatch

Share and Cite

MDPI and ACS Style

Luo, X.; Zhou, Z.; Li, B.; Zhang, Y.; Yi, C.; Shi, K.; Chen, S. Optimizing Steel Industry and Air Conditioning Clusters Using Coordination-Based Time-Series Fusion Transformer. Processes 2025, 13, 3265. https://doi.org/10.3390/pr13103265

AMA Style

Luo X, Zhou Z, Li B, Zhang Y, Yi C, Shi K, Chen S. Optimizing Steel Industry and Air Conditioning Clusters Using Coordination-Based Time-Series Fusion Transformer. Processes. 2025; 13(10):3265. https://doi.org/10.3390/pr13103265

Chicago/Turabian Style

Luo, Xinyu, Zhaofan Zhou, Bin Li, Yumeng Zhang, Chenle Yi, Kun Shi, and Songsong Chen. 2025. "Optimizing Steel Industry and Air Conditioning Clusters Using Coordination-Based Time-Series Fusion Transformer" Processes 13, no. 10: 3265. https://doi.org/10.3390/pr13103265

APA Style

Luo, X., Zhou, Z., Li, B., Zhang, Y., Yi, C., Shi, K., & Chen, S. (2025). Optimizing Steel Industry and Air Conditioning Clusters Using Coordination-Based Time-Series Fusion Transformer. Processes, 13(10), 3265. https://doi.org/10.3390/pr13103265

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