Enhancing Energy Efficiency in AI: A Multi-faceted Analysis Across Time Series, Semantic AI and Deep Learning Domains

Lejla Begic Fazlic (), Berkay Cetkin, Achim Guldner, Matthias Dziubany, Julian Heinen, Stefan Naumann and Guido Dartmann
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Lejla Begic Fazlic: Trier University of Applied Sciences
Berkay Cetkin: Trier University of Applied Sciences
Achim Guldner: Trier University of Applied Sciences
Matthias Dziubany: BITO CAMPUS GmbH
Julian Heinen: BITO CAMPUS GmbH
Stefan Naumann: Trier University of Applied Sciences
Guido Dartmann: Trier University of Applied Sciences

A chapter in Advances and New Trends in Environmental Informatics, 2025, pp 237-256 from Springer

Abstract: Abstract This research investigates strategies to enhance the energy efficiency of artificial intelligence (AI) algorithms, focusing on three pivotal domains: time series analysis, semantic AI, and deep learning (DL). Through a comprehensive examination of variables such as data size and the impact of hyper-parameter adjustments, the study aims to uncover nuanced insights into the relationship between algorithmic performance and energy consumption. By exploring the unique challenges and opportunities within each use case, this research provides valuable guidance for practitioners seeking to optimize energy efficiency in AI applications. The findings contribute to the ongoing discourse on sustainable AI development, offering practical overview to balance computational power with environmental considerations.

Keywords: Artificial intelligence; Energy efficiency; Machine learning; Sustainability (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-85284-8_14

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DOI: 10.1007/978-3-031-85284-8_14

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