Artificial Intelligence in Renewable Energy: A Pathway Toward an Adaptive, Equitable, and Sustainable Future – Systematic Review

Ahsan Habib¹*, Anisul Islam Opy²

AI enhances renewable energy efficiency, reliability, and sustainability, enabling predictive operations, optimized resource allocation, and equitable, climate-resilient energy systems.

Abstract

Artificial Intelligence (AI) has become a transformative force in reshaping how renewable energy systems operate, bridging the gap between environmental goals and technological capability. This systematic review explores how AI applications—spanning machine learning, deep learning, and optimization algorithms—are redefining energy generation, management, and distribution across solar, wind, and smart grid systems. The review draws on empirical evidence from various global initiatives, including those in Denmark, Germany, Australia, and the United States, to illustrate how AI enhances forecasting accuracy, grid stability, and operational efficiency. AI-driven forecasting models have notably improved energy prediction reliability, while predictive maintenance has minimized downtime and resource waste through real-time sensor analytics and anomaly detection. Beyond technical performance, AI supports smarter market participation, aligning energy supply with fluctuating demands to maximize both economic and environmental returns. AI-based forecasting models improve renewable energy prediction accuracy by 40–50%, enhance grid efficiency by 10–15%, and reduce maintenance costs by up to 30%. Predictive maintenance using sensor data and anomaly detection decreases equipment downtime by 25–35%. Additionally, AI-optimized market participation strategies increase energy revenues by 10–20% through intelligent demand–supply balancing and adaptive trading mechanisms. However, challenges remain—particularly regarding data quality, cybersecurity, and the opaque nature of complex AI models. Emerging trends such as explainable AI, digital twins, and edge computing show promise in addressing these barriers, ensuring greater transparency and resilience. Overall, this review underscores AI’s role as a catalyst for an intelligent, adaptive, and inclusive renewable energy ecosystem that not only accelerates the transition toward net-zero emissions but also integrates sustainability with social and ethical responsibility.

Keywords: Artificial Intelligence, Renewable Energy, Forecasting, Optimization, Smart Grids, Sustainability

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