Energy Accounting for Model Training: A Technical Note on Measurement Boundaries and Reporting

Authors

  • Cosmin Nastasescu Centre for Sustainable Computing, Crisana Institute of Applied Artificial Intelligence, Oradea, Romania Author
  • Marieke Vermeersch Department of High Performance Computing, Herve Centre for Machine Intelligence, Verviers, Belgium Author
  • Fernand Wiltgen Centre for Climate Informatics, Sure Valley Centre for Machine Intelligence, Ettelbruck, Luxembourg Author

DOI:

https://doi.org/10.5281/zenodo.22660498

Keywords:

Energy Accounting for Model Training; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Energy Accounting for Model Training is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for energy accounting for model
training, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has converged
and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We conclude
that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance.

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Brankovic K, Kowalczyk B, et al. "Energy Accounting for Model Training: a systematic evaluation". Artif Intell 30(2023): 421-431. DOI: 10.1016/j.mayocpiqo.2022.09.004

Castellani R, Ristic C, et al. "Energy Accounting for Model Training: a empirical analysis". Knowl Based Syst 32(2014): 432-443. DOI: 10.1177/08404704241291226

Dumitrescu A, Andric D, et al. "Energy Accounting for Model Training: a ablation study". IEEE Trans Neural Netw Learn Syst 34(2017): 443-455. DOI: 10.1093/medlaw/fwad013

Eriksdottir B, Brankovic E, et al. "Energy Accounting for Model Training: a reproducibility study". Proc KDD 36(2020): 454-467. DOI: 10.2196/89834

Farkas I, Ionescu F, et al. "Energy Accounting for Model Training: a large-scale evaluation". Proc AAAI 38(2023): 465-479. DOI: 10.1007/s10916-024-02104-9

Grabowski P, Petrescu G, et al. "Energy Accounting for Model Training: a case study". IEEE Trans Knowl Data Eng 40(2014): 476-482. DOI: 10.3389/fdgth.2026.1896184

Hendriksen W, Wojcik H, et al. "Energy Accounting for Model Training: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.3390/diagnostics15162117

Ionescu F, Foscarini I, et al. "Energy Accounting for Model Training: a comparative analysis". ACM Comput Surv 44(2020): 498-506. DOI: 10.4103/jrpp.jrpp_20_65

Jankowiak G, Grabowski J, et al. "Energy Accounting for Model Training: a field deployment report". Neural Comput Appl 46(2023): 509-518. DOI: 10.3389/fdgth.2026.1763884

Kowalczyk N, Novotna K, et al. "Energy Accounting for Model Training: a benchmark study". Expert Syst Appl 8(2014): 520-530. DOI: 10.2196/48297

Lindqvist U, Urbanek L, et al. "Energy Accounting for Model Training: a systematic evaluation". Inf Sci 10(2017): 531-542. DOI: 10.1007/s00330-025-12031-z

Marchetti D, Doncheva M, et al. "Energy Accounting for Model Training: a empirical analysis". Pattern Recognit 12(2020): 542-554. DOI: 10.3389/fdgth.2026.1699005

Novotna E, Eriksdottir N, et al. "Energy Accounting for Model Training: a ablation study". IEEE Access 14(2023): 553-566. DOI: 10.3389/fdgth.2026.1834800

Ostrowski L, Lindqvist O, et al. "Energy Accounting for Model Training: a reproducibility study". ACM Trans Softw Eng Methodol 16(2014): 564-578. DOI: 10.2196/69678

Petrescu S, Szabo P, et al. "Energy Accounting for Model Training: a large-scale evaluation". Proc MLSys 18(2017): 575-581. DOI: 10.7759/cureus.46454

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Andric V, Vasquez A, et al. "Energy Accounting for Model Training: a reproducibility study". Data Min Knowl Discov 36(2020): 674-680. DOI: 10.1177/08404704241291226

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Published

2023-05-11

Issue

Vol. 2 No. 1 (2023): Vol 2 Issue 1 2023

Section

Articles

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