The review identifies five major areas where AI techniques have accelerated system development and improved performance: power and energy forecasting, fault detection and maintenance, energy management, grid integration, and optimization. Across these areas, the authors describe a synergistic integration of AI with solar applications that has sped up development across the sector.

Two emerging technologies receive particular attention. Digital Twin systems, which create virtual replicas of physical solar installations, and Explainable AI, which the authors highlight as an emerging role alongside Digital Twins, are both identified as notable recent directions in the field.

The authors frame solar energy as a pivotal component of the global transition toward sustainable power, offering a robust alternative to fossil fuel systems with low environmental, ecological, and social impacts and contributing to energy security. Yet the review is candid about the barriers. AI deployment in solar systems confronts multifaceted technical, economic, environmental, and ethical challenges, including weather variability, high initial and operational costs, data security and privacy concerns, and potential job displacement.

Looking ahead, the review points to a next wave of AI technologies: Generative Artificial Intelligence, Causal AI, and Foundation Models, alongside a shift from centralized control toward autonomous and intelligent systems. If these transitions are navigated through responsible and innovative AI adoption, the authors conclude, they can further unlock the potential of AI in the solar sector and strengthen its role in advancing sustainable energy systems.

Subject of Research: Applications of artificial intelligence in solar energy systems

Article Title: AI-driven innovations in solar energy systems: advances, challenges, and emerging trends

Article References: Rezagholikhani, P., Rafiei, B., Raoufizadeh, H., Moshtaghi, P., & Hajialigol, N. (2026). AI-driven innovations in solar energy systems: advances, challenges, and emerging trends. Discover Sustainability. https://doi.org/10.1007/s43621-026-04957-1

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04957-1

Keywords: artificial intelligence, solar energy, photovoltaics, machine learning, Digital Twin, Explainable AI, energy forecasting, fault detection, grid integration, Generative AI, renewable energy, energy transition