Toward a Unified Fractal-AI Framework for Sustainable Energy Systems: A Review and Future Research Agenda

The increasing complexity of sustainable energy systems, driven by the large-scale integration of renewable resources, distributed generation, and multi-energy infrastructures, has created significant challenges in modeling, prediction, and system optimization. Conventional approaches, which often rely on linear assumptions and isolated modeling techniques, are insufficient for capturing the nonlinear, multi-scale, and highly dynamic behavior of modern energy systems. In this context, fractal theory and artificial intelligence have emerged as powerful and complementary paradigms for addressing these challenges, where fractal analysis enables the characterization of scale-invariant dynamics and long-range dependencies, while AI provides advanced capabilities for prediction, optimization, and control. However, existing research largely treats these approaches independently, resulting in fragmented methodologies that limit their effectiveness in complex energy environments. To address this gap, this paper presents a comprehensive review of fractal-based modeling and AI-driven techniques in sustainable energy systems and proposes a unified Fractal-AI framework that integrates multi-scale analysis, data-driven intelligence, and system-level optimization within a cohesive architecture. The proposed framework enables enhanced representation of complex system dynamics, improved prediction of energy behavior, and adaptive control under uncertainty. The paper further identifies key challenges and outlines future research directions for developing intelligent, resilient, and scalable energy systems based on the integration of fractal theory and artificial intelligence.