Explainable Multi-Task AI for Predicting EV Charging Demand

What is it about?

This paper presents a novel framework that predicts electric vehicle (EV) charging demand at a zonal level by simultaneously forecasting three key metrics: charging pile occupancy, charging volume, and charging duration. The approach leverages a spatiotemporal multi-task learning model that combines a temporal GraphSAGE module—capable of capturing both spatial relationships and time-dependent patterns—with dedicated task-specific layers. To enhance transparency, the framework uses an explainability technique called "mask-compute-analyze" along with Shapley values and small-world network theory. This allows researchers and practitioners to understand which features and regional interactions most influence the predictions. A case study in Shenzhen, China, demonstrates significant improvements over traditional single-task methods.

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Photo by Ernie Journeys on Unsplash

Photo by Ernie Journeys on Unsplash

Why is it important?

As EV adoption accelerates globally, efficiently managing charging infrastructure becomes critical. Accurate predictions of EV charging demand help optimize station layouts, resource allocation, and energy grid stability. This work is unique because it not only improves prediction accuracy by jointly learning related tasks but also addresses the “black box” issue of deep learning by providing clear, explainable insights into the model’s decision process. The combination of advanced graph-based learning with explainable AI techniques offers timely and practical benefits for urban planners, energy providers, and policymakers working towards sustainable transportation and smart city initiatives.

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