| M | T | W | T | F | S | S |
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | ||||
| 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| 11 | 12 | 13 | 14 | 15 | 16 | 17 |
| 18 | 19 | 20 | 21 | 22 | 23 | 24 |
| 25 | 26 | 27 | 28 | 29 | 30 | 31 |
The global energy system is undergoing a profound systemic and structural transformation, necessitating technological innovation to build a clean, efficient, and intelligent energy system. AI technology, with its powerful capabilities in data analysis, pattern recognition, and self-learning, is comprehensively penetrating the core links of the energy value chain. It is driving a paradigm shift in energy research from “experience-driven” to “data- and model-co-driven”, and has emerged as a core engine for unlocking system-wide energy efficiency improvements, supporting large-scale integration of renewable energy, and enabling dynamic and refined energy management. Its deep integration is poised to reshape both the research paradigms and industrial landscape of energy technology.
This special issue aims to focus on cutting-edge research related to AI-enabled high-efficiency energy utilization, storage, and management. It seeks to explore the deep application and challenges of AI technologies across various energy systems, thereby promoting the cross-disciplinary integration of energy and AI technologies.
The topics for submission include but are not limited to:
1. AI-enabled prediction of photovoltaic/wind power output and load forecasting;
2. AI-enabled thermodynamic modeling and energy management;
3. AI-enabled multi-physics simulation and solving complex thermofluidic problems;
4. AI-enabled development of new materials, working fluids, and components;
5. AI-enabled health state prediction and lifespan management of battery and thermal energy storage systems;
6. AI-enabled diagnosis, early warning, and digital twin of energy equipment;
7. AI-enabled integrated optimization and dispatch decision-making of integrated energy systems;
8. Application cases of AI technology in heat transfer enhancement, process optimization, equipment development, and system design
Prof. Jun Shen, Beijing Institute of Technology, China
Assoc. Prof. Jian Li, Beijing Institute of Technology, China
Assoc. Prof. Jingyu Cao, Hunan University, China
1. All papers will undergo a thorough peer review process. Accepted full papers that are presented at the conference will be published in proceedings.
2. The corresponding author is responsible for ensuring that the article’s publication has been approved by all other co-authors and takes responsibility for the paper during submission and peer review.
3. Review comments will be communicated to you and you may require to do necessary revisions and send revised paper on or before prescribed day.
Reviewers evaluate article submissions to IEECSC, based on the requirements of the conference proceedings, predefined criteria, and quality, completeness and accuracy of the research presented. They suggest improvements and make a recommendation to the editor about whether to accept, reject or request changes to the article. We sincerely welcome experts in the areas of Electrical Engineering and Energy join the conference as reviewer. If you are interested in joining IEECSC as Reviewer, please click the below button to submit your information.
Apply for ReviewerThe content of any submissions should be original and must not be submitted simultaneously for consideration towards publication in any other conference or journal. Reuse of material previously published by the authors is possible under the conditions that the authors fully disclose (cite) the reference and clearly highlight the innovative contribution of the IEECSC 2026 submission and the significance of this contribution.
Copyright © 2026 IEEE 3rd International Conference on Electrical Energy Conversion Systems and Control（IEECSC 2026）