Trade-Off Between Energy Consumption and Three Configuration Parameters in Artificial Intelligence (AI) Training: Lessons for Environmental Policy

Sri Ariyanti, Muhammad Suryanegara (), Ajib Setyo Arifin, Amalia Irma Nurwidya and Nur Hayati
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Sri Ariyanti: Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Indonesia
Muhammad Suryanegara: Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Indonesia
Ajib Setyo Arifin: Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Indonesia
Amalia Irma Nurwidya: Research Center for Process and Manufacturing Industry Technology, National Research and Innovation Agency (BRIN), Tangerang 15314, Indonesia
Nur Hayati: Department of Electrical Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta 55183, Indonesia

Sustainability, 2025, vol. 17, issue 12, 1-23

Abstract: Rapid advancements in artificial intelligence (AI) have led to a substantial increase in energy consumption, particularly during the training phase of AI models. As AI adoption continues to grow, its environmental impact presents a significant challenge to the achievement of the United Nations’ Sustainable Development Goals (SDGs). This study examines how three key training configuration parameters—early-stopping epochs, training data size, and batch size—can be optimized to balance model accuracy and energy efficiency. Through a series of experimental simulations, we analyze the impact of each parameter on both energy consumption and model performance, offering insights that contribute to the development of environmental policies that are aligned with the SDGs. The results demonstrate strong potential for reducing energy usage without compromising model reliability. The results highlight three lessons: promoting early-stopping epochs as an energy-efficient practice, limiting training data size to enhance energy efficiency, and developing standardized guidelines for batch size optimization. The practical applicability of these three lessons is illustrated through the implementation of a smart building attendance system using facial recognition technology within an Ecocampus environment. This real-world application highlights how energy-conscious AI training configurations support sustainable urban innovation and contribute to climate action and environmentally responsible AI development.

Keywords: AI; sustainability; environment; policy; energy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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