Artificial intelligence is playing a larger role in the clean energy transition than most project updates show. A review in the Journal of Big Data, covering more than 450 studies, maps AI across renewable forecasting, storage dispatch, hydrogen, bioenergy, carbon capture, and recycling. The authors argue that these systems are not just making individual technologies more efficient, they are reshaping how grids and industrial processes are designed and operated as connected systems. At the same time, the review is direct about what blocks deployment: sparse industrial data, hard to interpret models, cybersecurity risks, and aging grid and plant infrastructure that cannot easily accept probabilistic controls. It also flags the energy and emissions footprint of training large AI models. The proposed roadmap calls for open datasets, physics-informed models, governance frameworks, and closer alignment between technology, policy, and market signals. For anyone tracking whether AI actually cuts emissions or just adds compute demand, this is a useful synthesis.
