Artificial Intelligence for Green Energy: Case Studies in Materials Discovery, Perovskite Solar Cells, Hydrogen Production, and Grid Integration
Tanmay Saini
13/11/27
With the growing global demand for energy, the use of renewable power, like wind and solar, has revealed that traditional energy management approaches are falling short. This has made artificial intelligence (AI) a key technology for advancing green energy. This review collates and analyses 38 sources to assess the use of AI throughout the green-energy supply chain, specifically on the following case studies: Materials and Catalyst Discovery, Perovskite Solar Cell Manufacturing and Recycling, Optimization of Hydrogen and Biofuel Production, and Management of Renewable Electricity Grids. In each case, the review examines the key issue, provides an accessible explanation of relevant AI techniques, and provides a summary of evidence and an assessment of the robustness of that evidence. From the beginning of the candidate identification process to process optimization, and finally to real-time forecasting and control, the analysis shows that AI plays an increasingly important role throughout the chain. There are also changing requirements for models: They must be tolerant of error, transparent, and have access to data. In all instances, the combined models, in which data-driven learning is combined with physical knowledge or optimization algorithms, outperformed the single-method models. Explainable AI helps to improve scientific understanding and accountability. The evidence base, however, suffers from inconsistent measures, limited size of the data sets, weak comparisons and lack of large-scale use. Self-driving laboratories, shared datasets and strong validation systems will be key for the future to ensure trust in AI in energy systems.