Volume 7 • Issue 2 • PP: 28 –50 • 2026
Intelligent Energy Management: A Comprehensive Review of Artificial Intelligence, Machine Learning, and Metaheuristic Optimization Approaches
Open Access & Copyright
© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Intelligent energy management is increasingly important for improving energy efficiency, reliability, economic performance, and sustainability across buildings, smart homes, industrial facilities, microgrids, and interconnected energy systems. This review examines recent advances integrating machine learning, deep learning, reinforcement learning, Internet of Things technologies, predictive analytics, feature selection, and metaheuristic optimization. These methods support energy-consumption forecasting, demand response, renewable-energy coordination, load scheduling, anomaly detection, building optimization, smart-home control, industrial energy management, and microgrid operation. The reviewed studies indicate that intelligent approaches can improve forecasting accuracy, reduce energy costs, optimize resource allocation, and enhance real-time decision-making under uncertain conditions. Metaheuristic algorithms further improve performance through parameter optimization, feature selection, and multiobjective scheduling. However, practical deployment remains challenged by data quality, computational complexity, cybersecurity, privacy, interoperability, generalizability, transparency, infrastructure cost, and communication reliability. Future research should prioritize explainable, scalable, secure, and adaptive intelligent energy-management frameworks for efficient, resilient, flexible, and low-carbon energy system operation.
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