Data-Driven Learning and Disturbance Rejection Control in Energy Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".
Deadline for manuscript submissions: 20 June 2026 | Viewed by 8
Special Issue Editors
Interests: smart energy; application of machine learning technology in energy systems; multi-storage collaborative technology; carbon neutrality cutting-edge technology; thermal management technology for new energy systems; fuel cell optimization control (UAV & cogeneration Systems)
Special Issues, Collections and Topics in MDPI journals
Interests: uncertain artificial intelligence; process control; optimization and decision; system identification and modeling; edge computing
Interests: reinforcement learning control; active disturbance rejection control; data-driven control; distributed systems estimation and control; aircraft control theory and methods
Special Issue Information
Dear Colleagues,
The increasing complexity of modern energy systems, along with the urgent demand for carbon neutrality and sustainable operation, has posed significant challenges in terms of robustness, uncertainty handling, and multi-objective optimization. Traditional model-based control methods often struggle in high-dimensional, nonlinear, and strongly coupled systems. Constructing a high-fidelity model for energy systems is technically challenging and expensive. The newly emerging data-driven learning approaches offer new opportunities to overcome these limitations.
Data-driven techniques leverage operational data for system modeling, prediction, and optimization, while advanced disturbance rejection control enhances system robustness and stability under uncertainties. Their combination would provide a promising pathway for achieving intelligent, efficient, and sustainable energy systems. This is particularly relevant in areas such as smart energy management, multi-storage collaboration, carbon neutrality, thermal management for new energy systems, and fuel cell optimization.
This Special Issue aims to present the latest progress in data-driven learning and advanced control for energy systems, encompassing both theoretical advances and practical applications. We welcome high-quality original research papers, review articles, and case studies. Topics of interest include the following:
- Data-driven modeling and control methods: black-box modeling, system identification, reinforcement learning control;
- Disturbance modeling and rejection strategies: multi-source disturbance analysis, robust and adaptive control;
- Smart energy systems and intelligent scheduling: virtual power plants, distributed energy management, energy Internet;
- Machine learning and control co-design: algorithm–system optimization, deep learning-enabled control design;
- Multi-storage collaborative optimization: joint scheduling and stability of electricity–thermal–hydrogen systems;
- Carbon neutrality and low-carbon technologies: efficiency enhancement and optimization under emission constraints;
- Thermal management and safety: diagnosis and fault-tolerant control in batteries, fuel cells, and hybrid systems;
- Fuel cell modeling and optimization: robust operation for UAVs and cogeneration applications;
- Intelligent diagnostics and predictive maintenance: health management, remaining useful life prediction, and reliability;
- Cross-disciplinary approaches: integration of AI, control theory, and operations research in energy systems.
Prof. Dr. Li Sun
Dr. Qiu Liu
Dr. Wenchao Xue
Guest Editors
Manuscript Submission Information
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Keywords
- data-driven learning
- disturbance rejection control
- smart energy
- machine learning
- multi-storage
- carbon neutrality
- thermal management
- fuel cells
- intelligent diagnostics
- robust control
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