Volume 6 • Issue 1 • PP: 25 –30 • 2026
Price-Aware and Explainable Analytics for Urban Electric Vehicle Charging Networks: Forecasting Utilization Regimes for Sustainable Charging Operations
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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The efficient functioning of the electric-vehicle charging systems that are publicly operated has become focused on reliable short-horizon forecasting. The paper establishes an explainable and price-conscious analytical model to predict short-term charging usage and demonstrate the utility of tariff signals in an urban charging system. The analysis is based on UrbanEV benchmark, a new six months hourly panel of Shenzhen public charging infrastructure, which integrates occupancy, charging time, charging volume, electricity tariffs, service charges, weather and spatial descriptors. The concept of charging occupancy is considered an operation state variable with connection to queue exposure, reliability of service, and tactical intervention. A succinct mathematical formulation is created to use it in one-step-ahead utilization forecasting and in interpreting low-, medium-, and high-utilization regime. The empirical analysis is pegged to benchmark evidence reported to UrbanEV, where transformer-based forecasting had the best node-level performance and TimeXer had the best RMSE values of 0.07 in occupancy, 2.73 in charging duration, and 43.66 in charging volume. Further discussion indicates that occupancy prediction is accurate enough to justify regime based intervention and strongest additional gains are obtained through the joint effect of pricing variables and temperature-price interactions as opposed to single covariates. The results justify the justifiable, price-conscious forecasting as an operational decision tool to alleviate congestion, design tariffs and specific capacity planning in sustainable charging networks.
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