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Tri-modal Causal Learning for Forecasting EV Charging Demand

delete2026-04-30
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PRE
AI
X
Xiaoping Wang
Q
Qianqian Ren *
H
Hezhe Wang *
Z
Zhijuan Li
DOI：10.1016/j.patrec.2026.04.028delete
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Abstract

Abstract

En 中文
Highlights* • Leverages tri-modal learning for EV demand forecasting in smart city energy systems. • Captures semantic spatial relations to enhance transferability across urban zones. • Ensures causal temporal modeling for reliable real-time deployment applications. • Integrates electricity pricing signals to reflect user behavior and economic impact. • Supports intelligent mobility optimization with up to 9.8% error reduction achieved.
Keywords:
EV demand forecasting
tri-modal learning
causal modeling
smart city energy systems
electric vehicle charging

Journal

Pattern Recognition Letters cover
Pattern Recognition Letters
IF:
3.3
Papers:
7.8K
Citations:
1.6W

Organization

H
Heilongjiang University
Scholars:
8.1K
Papers: 5.1K
Citations: 6.8K
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