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Modélisation et prédiction des performances environnementales des camions frigorifiques en utilisant des données réelles et la science des données.

Modelling and prediction of refrigerated trucks’ environmental performance using real-life data and data science.

Numéro : 1078

Auteurs : MARCY M., FERTEL C., PETIT J. M., SCUTURICI M., CAVALIER G., BONJOUR J.

Résumé

Although the study of the environmental impact of refrigerated vehicles as a whole entity is increasingly important for sector actors in the context of climate change mitigation, it remains in its early stages. The majority of published research in this domain relies on numerical models based on physics and thermodynamic laws. Despites the numerous advantages of this approach, it generally excludes real-life effects and multicollinearity between factors not captured in those laws. The application of data-science techniques on real-world data can greatly help address this, but is unfortunately still very limited due to the major obstacles to the adoption of data science small and medium organisations are facing. An analytic system, REFTRAN, is currently under construction, combining data on real-world vehicles, analytical and modelling methods. This system enables the acceleration and improvement of a wide range of analyses on refrigerated trucks and their long-term environment impact by ensuring a quick access to up-to-date high-quality data. This communication presents the first version of this analytical system along with some of the analyses enabled by it.

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Format PDF

Pages : 10 p.

Disponible

  • Prix public

    20 €

  • Prix membre*

    Gratuit

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Détails

  • Titre original : Modelling and prediction of refrigerated trucks’ environmental performance using real-life data and data science.
  • Identifiant de la fiche : 30032527
  • Langues : Anglais
  • Sujet : Environnement
  • Source : 8th IIR International Conference on Sustainability and the Cold Chain. Proceedings: June 9-11 2024
  • Date d'édition : 11/06/2024
  • DOI : http://dx.doi.org/10.18462/iir.iccc2024.1078

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