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Deep learning model predicts trolleybus energy use, identifies savings factors

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel time-aware tabular deep learning framework to predict energy consumption for dual-source trolleybuses. This model integrates periodic time encoding and a Bayesian optimization approach to learn from static and sequential features, outperforming existing methods. Beyond prediction, the framework includes a causal explanation pipeline to identify key factors influencing energy usage, such as regenerative braking and average speed, offering insights for operational improvements. AI

IMPACT Offers actionable insights for optimizing energy consumption in transportation systems through advanced predictive modeling.

RANK_REASON Academic paper detailing a new deep learning architecture and causal analysis for a specific domain. [lever_c_demoted from research: ic=1 ai=0.7]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning model predicts trolleybus energy use, identifies savings factors

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Academic paper detailing a new deep learning architecture and causal analysis for a specific domain. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    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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