International Journal of Research in Advanced Electronics Engineering
2026, Vol. 7, Issue 1, Part A
AI-driven energy management systems: A critical analysis of methodologies, applications, and systemic implications
Author(s): Moawia Ibrahim Ahmed Hamed
Abstract: The review paper at hand presupposes the synthesis of existing empirical data and existing research to profoundly study the Artificial Intelligence-based Energy Management Systems (AI-EMS) at the doctoral level of research. The paper discusses the essential AI paradigms, such as supervised learning, deep learning, reinforcement learning, and hybrid-based development, which are the basis of recent energy management architectures. The review quantifies the improvement of the performance through the systematic analysis of the applications of the building energy management, microgrid operations, and the coordination of the demand response and grid-edge control, and critically evaluates the issue of implementation. Specific attention is paid to new systemic challenges, such as the energy footprint of AI systems in particular, the threat of algorithmic market, and socio-technical barriers to fair implementation. As it was revealed in the analysis, AI-EMS is valid in improving operational efficiency, integrating renewables, and predictability, but the achievement of implementing AI can be achieved with the assistance of structured data governance systems, explainable AI solutions, and policy interventions. The paper will also end with some recommendations on how researchers, practitioners, and policymakers can proceed to make AI-EMS responsibly use AI-EMS to mitigate the risks. The results indicate that AI is an innovative and dual-sided technology in the energy industry that must be created with the benefits of calculations against the harms to the environment and society.
DOI: 10.22271/27084558.2026.v7.i1a.69
Pages: 01-06 | Views: 86 | Downloads: 32
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How to cite this article:
Moawia Ibrahim Ahmed Hamed. AI-driven energy management systems: A critical analysis of methodologies, applications, and systemic implications. Int J Res Adv Electron Eng 2026;7(1):01-06. DOI: 10.22271/27084558.2026.v7.i1a.69
