Guest Editors
Prof. Khan Muhammad Adnan
Email: adnan@gachon.ac.kr
Affiliation: Department of Software, faculty of Artificial Intelligence and Software, Gachon University, Seongnam-Si, Republic of Korea
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Research Interests: computational intelligence, federated learning, application of artificial intelligence, machine learning, smart energy storage and prediction systems
Summary
The global transition toward smart, low-carbon, and resilient energy systems has accelerated the adoption of advanced digital technologies for energy modeling, optimization, and control. The increasing integration of renewable energy sources, distributed energy resources, electric vehicles, and intelligent buildings has significantly increased system complexity and uncertainty. Conventional analytical and rule-based approaches are often insufficient to address the nonlinear, stochastic, and large-scale nature of modern energy systems. In this context, computational intelligence methods—such as machine learning, deep learning, evolutionary algorithms, swarm intelligence, and hybrid intelligent techniques—have emerged as powerful tools for enhancing decision-making and operational efficiency in smart energy systems.
The aim of this Special Issue is to present state-of-the-art research and practical applications of computational intelligence methods for the modeling, optimization, and control of smart energy systems. It seeks to bridge theoretical advances and real-world deployments, promoting intelligent, adaptive, and data-driven solutions that improve energy efficiency, reliability, sustainability, and system resilience. The scope covers smart grids, microgrids, renewable energy integration, intelligent buildings, and energy management systems across both centralized and decentralized environments.
Suggested themes include, but are not limited to:
· Computational intelligence for energy system modeling and forecasting
· Intelligent optimization of smart grids and microgrids
· AI-based control strategies for energy management systems
· Renewable energy integration using intelligent methods
· Demand response and load prediction
· Intelligent control of HVAC and building energy systems
· Hybrid and bio-inspired algorithms for smart energy applications
· Real-world case studies and industrial implementations
Keywords
computational intelligence, energy systems, federated learning, machine learning, deep learning, smart grids and micro grids, energy management system, load prediction
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