Review maps AI roles in renewable-energy modeling and transition pathways

A systems-oriented review concludes that artificial intelligence is being used across solar, hydropower and wind systems for forecasting, optimization, predictive maintenance and real-time control. The review also finds that deep learning dominates forecasting applications, while reinforcement learning and hybrid AI–physics frameworks show potential for adaptive control, digital twins and intelligent energy management. These conclusions are reported in the article’s abstract; the supplied evidence does not include the full review or its underlying study-selection and quality-assessment methods.

The review distinguishes itself from technical algorithm reviews that assess computational performance in isolation. Its stated framework organizes AI techniques by operational function, deployment considerations and contribution to long-term sustainability outcomes. It synthesizes machine learning, deep learning, reinforcement learning, fuzzy logic and hybrid AI–physics approaches across solar, hydropower and wind energy applications.

Four functional roles are examined: forecasting, optimization, maintenance and control. The synthesis reports substantial improvements in predictive performance for deep learning approaches in forecasting. Reinforcement learning and hybrid AI–physics models are described as having strong potential for adaptive control, digital twins and intelligent energy management. The review identifies hybrid frameworks as offering a balance among predictive accuracy, transparency and practical deployment feasibility, although the abstract does not provide comparative values, uncertainty estimates or the number of studies underlying that assessment.

Deployment factors considered in the review include scalability, interpretability, computational requirements and implementation readiness. The authors also connect AI capabilities with renewable-energy integration, resource efficiency, system resilience and decarbonization, framing AI as a sustainability-enabling technology within energy-transition planning. However, the available evidence does not quantify emissions reductions, cost effects, reliability changes or long-term infrastructure performance, and it does not establish that AI caused any specific sustainability outcome. The article is an open-access review, but the publisher’s supplied page identifies the displayed version as accepted research shared early and subject to further editorial edits, so the findings summarized here should be understood as an abstract-level account rather than a full appraisal of the complete review.

Subject of Research: Systems-oriented review of artificial intelligence applications in renewable-energy modeling and transition pathways

Article Title: Systems oriented review on artificial intelligence as a sustainability enabler in renewable energy modeling and energy transition pathways

Article References: Salinas, J. V., Calderon, A., Macayan, J., & Ofalia, B. (2026). Systems oriented review on artificial intelligence as a sustainability enabler in renewable energy modeling and energy transition pathways. Discover Sustainability. https://doi.org/10.1007/s43621-026-04850-x

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04850-x

Keywords: artificial intelligence, renewable energy, energy modeling, energy transition, machine learning, deep learning, reinforcement learning, hybrid AI–physics models, sustainability, wind energy, solar energy, hydropower