ARTIFICIAL INTELLIGENCE-DRIVEN ENERGY MANAGEMENT SYSTEMS FOR SUSTAINABLE DECARBONIZATION: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS

Mohammed Abdalghafoor

doi:10.5281/zenodo.21312392

Abstract

The shift to carbon neutrality has speeded up in the world, and the use of intelligent energy management systems that optimize energy production, energy distribution, storage and energy consumption have increased. Artificial Intelligence (AI) is a game-changer for boosting efficiency, integrating renewables, and guiding energy sector decision-making in today's energy systems. The review explores the potential of AI-based Energy Management Systems (EMSs) to aid in the progress of sustainable decarbonization, summarizing recent advances in machine learning, deep learning, reinforcement learning, predictive analytics, and digital twin technologies. It explores significant opportunities such as real-time energy optimization, demand forecasting, grid resilience, and reduction of emissions, along with the critical challenges of data quality, cybersecurity, transparency of models, scalability and regulatory compliance. The review also identifies future research avenues, particularly focused on explainable AI, edge intelligence, federated learning, and holistic policy frameworks, to propel the creation of secure, efficient, and sustainable low-carbon energy ecosystems.

Keywords

Artificial intelligence, energy management systems, sustainable decarbonization, renewable energy integration, smart grid optimization.

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