Smart Metering and Machine Learning for the Quantification of Regenerative Braking Energy on the d.c. Railway Systems
- DOI
- 10.1016/j.trpro.2023.11.778
- Published
- 2023
- Container
- Transportation Research Procedia
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.trpro.2023.11.778,
title = {Smart Metering and Machine Learning for the Quantification of Regenerative Braking Energy on the d.c. Railway Systems},
author = {Nouha JAOUA and Smail ZIANI and Ayman AGOUBE and Olivier LANGLOIS and Michal TARATAJCIO},
year = {2023},
journal = {Transportation Research Procedia},
doi = {10.1016/j.trpro.2023.11.778},
url = {https://doi.org/10.1016/j.trpro.2023.11.778}
}RIS
TY - JOUR TI - Smart Metering and Machine Learning for the Quantification of Regenerative Braking Energy on the d.c. Railway Systems AU - Nouha JAOUA AU - Smail ZIANI AU - Ayman AGOUBE AU - Olivier LANGLOIS AU - Michal TARATAJCIO PY - 2023 JO - Transportation Research Procedia DO - 10.1016/j.trpro.2023.11.778 UR - https://doi.org/10.1016/j.trpro.2023.11.778 ER -
APA
JAOUA, N., ZIANI, S., AGOUBE, A., LANGLOIS, O., & TARATAJCIO, M. (2023). Smart Metering and Machine Learning for the Quantification of Regenerative Braking Energy on the d.c. Railway Systems. Transportation Research Procedia. https://doi.org/10.1016/j.trpro.2023.11.778
Source records
- crossref · retrieved 2026-11-08T03:49:40.011Z