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Deep learning model predicts trolleybus energy use and causal factors

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel time-aware tabular deep learning framework to predict energy consumption for dual-source trolleybuses. This model not only forecasts energy usage between stops but also quantifies the causal drivers of that consumption. Experiments on a Zurich trolleybus dataset demonstrated significant accuracy improvements over existing methods, identifying factors like regenerative braking and average speed as key energy-saving elements. AI

IMPACT This research could lead to more efficient energy management systems for public transportation, reducing operational costs and environmental impact.

RANK_REASON The cluster contains an academic paper detailing a new deep learning architecture and its application to a specific problem domain.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Deep learning model predicts trolleybus energy use and causal factors

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · 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 Artif… ·

    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. Existing models struggle…

  2. arXiv cs.LG TIER_1 English(EN) · Jun Gong ·

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

    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. Existing models struggle to represent these heterogeneous inputs and rarel…

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