Data-Centric Green AI An Exploratory Empirical Study

Roberto Verdecchia, Luis Cruz, June Sallou, Michelle Lin, James Wickenden, Estelle Hotellier

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
2 Downloads (Pure)


With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing concern. To address this issue, in recent years, studies have focused on demonstrating how AI energy efficiency can be improved by tuning the model training strategy. Nevertheless, how modifications applied to datasets can impact the energy consumption of AI is still an open question.To fill this gap, in this exploratory study, we evaluate if data-centric approaches can be utilized to improve AI energy efficiency. To achieve our goal, we conduct an empirical experiment, executed by considering 6 different AI algorithms, a dataset comprising 5,574 data points, and two dataset modifications (number of data points and number of features).Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced (up to 92.16%), often at the cost of a negligible or even absent accuracy decline. As additional introductory results, we demonstrate how, by exclusively changing the algorithm used, energy savings up to two orders of magnitude can be achieved.In conclusion, this exploratory investigation empirically demonstrates the importance of applying data-centric techniques to improve AI energy efficiency. Our results call for a research agenda that focuses on data-centric techniques, to further enable and democratize Green AI.
Original languageEnglish
Title of host publicationProceedings of the 2022 International Conference on ICT for Sustainability (ICT4S)
EditorsCoral Calero, Andy Karvonen, Elena Somova, Joao Paulo Fernandes, Anne-Kathrin Peters, Jacome Cunha
Place of PublicationPiscataway
Number of pages11
ISBN (Electronic)978-1-6654-8286-8
ISBN (Print)978-1-6654-8287-5
Publication statusPublished - 2022
Event2022 International Conference on ICT for Sustainability (ICT4S) - Plovdiv, Bulgaria
Duration: 13 Jun 202217 Jun 2022

Publication series

NameProceedings - 2022 International Conference on ICT for Sustainability, ICT4S 2022


Conference2022 International Conference on ICT for Sustainability (ICT4S)
City Plovdiv

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Energy Efficiency
  • Artificial Intelligence
  • Green AI
  • Data-centric


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