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

In: Advances and New Trends in Environmental Informatics

Author

Listed:
  • 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)

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.

Suggested Citation

  • Lejla Begic Fazlic & Berkay Cetkin & Achim Guldner & Matthias Dziubany & Julian Heinen & Stefan Naumann & Guido Dartmann, 2025. "Enhancing Energy Efficiency in AI: A Multi-faceted Analysis Across Time Series, Semantic AI and Deep Learning Domains," Progress in IS, in: Volker Wohlgemuth & Hamdy Kandil & Amna Ramzy (ed.), Advances and New Trends in Environmental Informatics, pages 237-256, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-85284-8_14
    DOI: 10.1007/978-3-031-85284-8_14
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