Table of Content

Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems

Submission Deadline: 31 July 2024 Submit to Special Issue

Guest Editors

Prof. Dr. Wei-Chiang Hong, Asia Eastern university of Science and Technology, Taiwan / Yuan Ze University, Taiwan
Assco. Prof. Dr. Yi Liang, Hebei GEO University, China


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


• Artificial intelligence
• Machine learning
• Energy systems
• Renewable energy
• Demand & forecasting
• Energy generation, transmission, and distribution
• Energy data analysis
• Energy management

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