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Hugging Face Trending Papers

Time-Aware Validation of Machine Learning Fuel Consumption Models: Evidence from 1\,Hz Operational Data, CCGS \textit{Sir Wilfrid Laurier}

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arXiv Machine Learning
Aug 18

Time-Aware Validation of Machine Learning Fuel Consumption Models: Evidence from 1\,Hz Operational Data, CCGS \textit{Sir Wilfrid Laurier}

arXiv:2608. 16833v1 Announce Type: new Abstract: Ship fuel consumption (SFC) prediction supports vessel operation optimisation, emissions estimation, and decision support systems (DSS) for sustainable maritime transportation.

By Samarasimha Reddy Chittamuru, Ayhan Akinturk, Allison Kennedy, Joshua Barnes, Matthew Hamilton
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arXiv Machine Learning
Jun 5

Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data

arXiv:2606. 06156v1 Announce Type: new Abstract: Machine learning-based predictive emissions monitoring systems offer a practical alternative to direct emissions measurement, but their deployment across gas turbine fleets is challenging when emissions labels are available for only a small subset of assets.

By Rebecca Potts, Aiden Durrant, Rick Hackney, Georgios Leontidis
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arXiv Machine Learning
Jul 14

Inter-Stop Energy Prediction and Causal Driver Quantification for Dual-Source Trolleybuses via a Time-Aware Tabular Deep Learning Architecture

arXiv:2607. 11349v1 Announce Type: cross Abstract: Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajectories, and hourly weather.

By Wentao Zeng (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China, School of Mechanical and Electrical Engineering and Automation, Foshan University, Foshan, China), Zijian Huang (School of Artificial Intelligence, South China Normal University, Guangzhou, China), Yiming Bie (School of Transportation, Jilin University, Changchun, China), Jiabin Wu (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China), Jun Gong (Department of Civil Engineering, The University of Hong Kong, Hong Kong, China)
benchmarks
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arXiv Machine Learning
Jun 4

RIDE: An Open Dataset and Benchmark for Train Delay Prediction

arXiv:2606. 05070v1 Announce Type: new Abstract: Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized datasets, prediction targets, and evaluation protocols.

By Cl\'ement Elliker, Mathis Le Bail, Cl\'ement Mantoux, Jesse Read, Sonia Vanier
benchmarksdata-engineering
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Hugging Face Trending Papers
Jul 21

Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data

In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations. Our results show that Bayesian linear regression based on this physics-aware model can improve the reliability of the expected energy consumption, as compared with standard linear regression.

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