Authors: Vaishnavi Vadane, Abhishek P. Bangar, Sachin B. Bhosale, V. A. Karad
Abstract: Industrial production depends on electricity, process heat, motors, pumps, compressed-air networks, refrigeration, and production schedules. Artificial Intelligence (AI) can help identify avoidable energy use and support decisions that reduce associated greenhouse-gas emissions. This review examines Green AI strategies for industrial energy and carbon reduction, including energy forecasting, predictive maintenance, process optimisation, intelligent scheduling, digital twins, renewable-energy coordination, and carbon-aware operation. It also considers the environmental cost of AI itself, such as energy used for model training and inference, data storage, networking, and hardware. The reviewed literature indicates that AI-enabled monitoring, prediction, control, scheduling, and parameter optimisation are important application areas. However, reported benefits depend on data quality, system integration, baseline selection, operator acceptance, and verification in real operating conditions. The central research gap is the lack of a consistently applied, independently verifiable framework that measures net energy and carbon benefits across the industrial system while accounting for the AI system’s own footprint and comparing AI with conventional engineering alternatives. This paper organises the gaps, proposes research questions, and presents a practical framework for future work.
