Artificial Intelligence-Driven Renewable Energy Systems: A Targeted Review of Key Applications and the Emerging Role of OASIS Colab as a Collaborative Research Environment
Authors: Tewfik BENCHATTI, Abdeljalil HocineCHENAFI, khaledLAMINE
DOI: 10.37326/ajsev8.12/2069
Page No: 115-137
Abstract
The accelera ng global transi on toward renewable energy systems has intensified the need for intelligent, data-driven methodologies capable of addressing challenges related to variability, uncertainty, and large-scale system integra on. In this context, ar ficial intelligence (AI) has emerged as a key enabler for enhancing the performance, reliability, and opera onal efficiency of renewable energy technologies. This paper presents a targeted and structured review of 50 high-impact peerreviewed studies that collec vely define the current state-of-the-art of AI-driven renewable energy systems. The review systema cally examines core AI paradigms including machine learning, deep learning, and reinforcement learning and their prac cal applica ons in renewable energy genera on forecas ng, demand predic on, predic ve maintenance, energy storage op miza on, and intelligent management of smart and decentralized energy networks. Par cular a(en on is given to solar and wind energy systems, where AI-based models have demonstrated significant improvements in forecas ng accuracy, system resilience, and adap ve control under dynamic opera ng condi ons. Beyond algorithmic developments, this study adopts a data-centric perspec ve to highlight the growing importance of scalable, reproducible, and collabora ve research environments in advancing AI-enabled energy research. Within this framework, OASIS Colab is discussed as a representa ve collabora ve AI research environment that supports large-scale experimenta on, facilitates the integra on of heterogeneous energy datasets, and enhances the reproducibility and transparency of AI workflows. Rather than func oning as a standalone solu on, OASIS Colab is posi oned as a computa onal research enabler that assists researchers in bridging the gap between theore cal AI models and prac cal renewable energy applica ons. Emerging research direc ons including hybrid physics-informed learning models, digital twin–assisted energy systems, and advanced reinforcement learning strategies for grid and storage management are also analyzed for their poten al to reshape future renewable energy infrastructures. Finally, the review iden fies key technical, data-related, computa onal, and regulatory challenges that currently limit the widespread adop on of AI in renewable energy systems and outlines research-oriented recommenda ons aimed at maximizing the impact of AI-enabled methodologies and collabora ve research environments on sustainable energy transi ons and long-term climate change mi ga on.
