Organizations: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · Intelligent Transportation Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511458, China
Electric vehicle (EV) charging loads exhibit strong behavioral heterogeneity and temporal variability, posing significant challenges for online probabilistic forecasting under evolving operating conditions. In particular, persistent charging patterns may differ substantially across stations, while recent behavioral changes can continuously alter the underlying load distributions. This paper proposes a behavior-guided online probabilistic forecasting framework that explicitly characterizes persistent station-specific patterns and recent behavioral changes. A dual-timescale behavior representation is constructed to distinguish long-term charging characteristics from recent behavioral states and quantify their deviations. These behavioral changes are further semantically encoded to guide drift-aware forecasting adaptation, while a delayed-feedback mechanism ensures temporally consistent online updates when observations become available across different forecasting horizons. Experiments on ten heterogeneous real-world charging stations demonstrate that the proposed method consistently outperforms conventional forecasting models and concept-drift-aware online baselines in forecasting accuracy and probabilistic reliability. For 1-h-ahead forecasting, the proposed method reduces MSE and Pinball loss by 15.3% and 17.8%, respectively, over the corresponding best baselines. For 4-h-ahead forecasting, the improvements further reach 16.8% and 22.6%, respectively, demonstrating consistent performance gains under evolving charging behaviors and extended forecasting horizons.
Figures & tables
Fig. 1 : Heterogeneous charging behaviors across different EV types.
Fig. 2 : Framework of the proposed behavior-guided online probabilistic forecasting.
Fig. 3 : Delayed-feedback online forecasting with horizon H=3 .
Fig. 4 : Semantic prompts for long-term and recent charging behaviors.
Fig. 5 : Heterogeneous and evolving EV charging behaviors across stations.
Fig. 6 : Online probabilistic forecasts across heterogeneous EV charging stations.
Fig. 7 : Ablation analysis of behavior-guided forecasting components.
Fig. 8 : Station-wise ablation analysis of behavior-guided components.
Fig. 9 : Station-wise gains of semantic over numerical behavior encoding.
Model
Params.
Inference
Update
P95
Memory
(M)
(ms/sample)
(ms/step)
(ms)
(MB)
TCN
0.06
0.61
3.48
4.58
18.2
PatchTST
0.60
2.15
9.24
14.79
11.4
LSTM
0.05
0.21
2.97
4.21
1.8
FSNet
0.07
0.84
4.51
6.70
2.1
OneNet
0.13
1.42
6.99
11.30
3.0
TABLE II : Computational efficiency and resource consumption.
Fig. 10 : Forecasting gains across different behavior-drift intensities.
Encoder
Behavior
1 h ahead
4 h ahead
MSE ↓
Pinball ↓
PICP@90
MSE ↓
Pinball ↓
PICP@90
LSTM
Without
0.674
0.164
0.751
0.896
0.225
0.743
LSTM
With
0.360
0.097
0.898
0.412
0.106
0.897
TCN
Without
0.653
0.173
0.751
0.984
0.239
0.703
TCN
With
0.378
0.103
0.891
0.446
0.116
0.886
PatchTST
Without
0.678
0.179
0.812
0.923
0.212
0.768
TABLE III : Evaluating the scalability of behavior-guided forecasting.
Name
Setting
Offline pretraining
Initial training period
60 days
Pretraining epochs
30
Batch size
128
Optimizer
AdamW
Initial learning rate
3×10−4
TABLE IV : Training and Online Adaptation Settings
Uncertainty quantification for electric vehicle (EV) charging demand is essential to turn the increasing pressure of EV adoption on power systems into a source of flexibility. We study a large-scale dataset of public charging stations in Scotland, where demand exhibits a hierarchical structure, organized across multiple aggregation levels, from individual charging points to regional and national scales. The charging infrastructure is continuously evolving, with new stations deployed and existing ones occasionally removed, which calls for forecasting and uncertainty quantification methods tailored to this evolving structure. To address these challenges, we combine hierarchical forecasting with recent advances in conformal prediction with missing data. The conformal procedures considered produce valid prediction intervals at every node of the hierarchy and the algorithm CP-MNR-Nested successfully accounts for the time-varying hierarchical structure of the data.
Guillaume Principato, Yvenn Amara-Ouali, Yannig Goude +2
Laboratoire de Mathematiques d’Orsay, Université Paris-Saclay, CNRS, INRIA · Electricité de France R&D · Université Paris-Cité
Accurate forecasting of electric vehicle (EV) charging demand is critical for grid management and infrastructure planning. Yet the field continues to rely on legacy benchmarks; such as the Palo Alto (2020) dataset; that fail to reflect the scale and behavioral diversity of modern charging networks. To address this, we introduce a novel large-scale longitudinal dataset collected across Scotland (2022 2025), which release it as an open benchmark for the community. Building on this dataset, we formulate EV charging demand as a spatio-temporal latent Gaussian field and perform approximate Bayesian inference via Integrated Nested Laplace Approximation (INLA). The resulting model jointly captures spatial dependence, temporal dynamics, and covariate effects within a unified proba bilistic framework. On station-level forecasting tasks, our approach achieves competitive predictive accuracy against machine learning baselines, while additionally providing principled uncertainty quan tification and interpretable spatial and temporal decompositions properties that are essential for risk-aware infrastructure planning.
Kaoutar Bouaachra, Yvenn Amara-Ouali, Yannig Goude +1
École Polytechnique de Paris · EDF Lab Paris-Saclay Palaiseau, France · INRIA Paris, DYOGENE/MATHNET Team
The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability. Smart control of EV charging -- e.g., based on reinforcement learning (RL) -- can alleviate these issues by learning temporal and contextual patterns from historical data. Yet, in real-world scenarios, key features, such as departure time, often are unavailable. This, in turn, makes it harder for an RL agent to learn and execute an effective charging policy. To mitigate this uncertainty, a trained forecaster can approximate the unknown features from available data. However, since these forecasting models are typically trained for accuracy (rather than their impact on a downstream agent's decision quality), their errors may propagate and hinder the overall performance of a controller that is using the forecasts. To avoid this, we propose a decision-focused RL (DF-RL) framework in which the forecaster is trained end-to-end, i.e., with feedback from the charging policy actions taken by the RL agent. Such joint training of both the forecaster and controller ultimately results in higher-quality actions: our proposed DF-RL method yields superior charging decisions compared to other baselines, achieving up to a 14% improvement in total reward and a 55% reduction of unsupplied energy (i.e., charging that failed to happen because the EV already left), relative to the RL method without departure time forecasting.
Giuseppe Gabriele, Fabio Pavirani, Seyed Soroush Karimi Madahi +1