Submission Deadline: 30 November 2026 View: 91 Submit to Special Issue
Prof. Dr. Wei-Chiang Hong
Email: samuelsonhong@gmail.com
Affiliation: College of Shipbuilding Engineering, Harbin Engineering University, Harbin, China
Research Interests: computational intelligence, the application of forecasting technology, machine learning algorithms
Assoc. Prof. Yi Liang
Email: louisliang@hgu.edu.cn
Affiliation: School of Management, Hebei GEO University, Shijiazhuang, China
Research Interests: artificial intelligence computing, technical forecasting and evaluation
Prof. Dr. Ming-Wei Li
Email: limingwei@hrbeu.edu.cn
Affiliation: College of Shipbuilding Engineering, Harbin Engineering University, Harbin, China
Research Interests: ship motion forecasting, meta-heuristic algorithms, scheduling optimization
Dr. Zhong-Yi Yang
Email: yangzhy@zuwe.edu.cn
Affiliation: College of Economics & Management, Zhejiang University of Water Resources and Electric Power, Hangzhou, China
Research Interests: container port scheduling optimization, meta-heuristic algorithms, scheduling optimization
Diverse energy and power systems have been playing a significantly critical role in the revolution of sustainable energy supply for the future, such as gas turbines, wind turbines, photovoltaic panels, building heating, ventilation and air-conditioning (HVAC) systems, etc., which have a great impact on energy resources and efficiencies. Due to the emerging artificial intelligence and machine learning, traditional modeling techniques in these energy systems have met challenges in still leveraging physics model and first principle-based approaches. Moreover, with the rapid development of hardware and computing techniques, new modeling approaches for energy systems have become more and more important for system design, integration, analysis, control, and management. This Special Issue aims to present and disseminate the most recent advances related to modeling theory, approaches, and applications of energy systems.
Topic of interests for publication include but are not limited to:
• Energy demand forecasting and management
• Optimization of renewable energy systems
• Predictive maintenance of energy equipment & systems
• Prediction and prevention applied to energy system
• Analysis and interpretation of energy data for decision making purposes
• Intelligent energy management systems
• Deep Learning for Predictive Maintenance in Renewable Energy Systems
• Artificial Neural Networks for Optimal Operation of Microgrid Systems
• Application of Deep Learning for Improving the Efficiency of Distributed Energy Resources
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