AYTAÇ UĞUR YERDEN

Green AI for Sustainable Energy Storage

Abstract

This study aims to investigate the impact of green AI and energy storage systems on reducing the environmental impact of generative AI and large language models. Literature shows findings on software selection through prompt engineering or hardware selection through model training. The study examines the impact of more energy-efficient green AI algorithms, which are the focus of sustainable AI systems, particularly on energy storage systems, whose importance is increasing today, by analyzing battery management systems, state of charge (SoC), and state of health (SoH) parameters. The findings show that when models are not trained and run on the necessary part of the data for AI algorithm optimization, energy consumption increases. Future studies aim to focus on battery thermal management and system structures to overcome these limitations and improve capacity.

Biography

AYTAC UGUR YERDEN obtained his B.Sc. degrees in Electrical and Electronics Engineering from Bursa Technical University and Electricity Education from Marmara University. He received his M.Sc. and Ph.D. degrees in Mechatronics Engineering from Marmara University. His research interests include cognitive systems, machine learning, deep learning, renewable energy systems, and computer vision. He is currently an Assistant Professor in the Faculty of Engineering and the Head of the Artificial Intelligence Engineering Department at Istanbul Gedik University.