EV Charging Demand Forecasting

Updated 26 December 2025

EV charging demand forecasting is the quantitative prediction of energy use at charging sites, capturing temporal, spatial, and behavioral variability.
Advanced methods including LSTM, Transformers, and graph neural networks have demonstrated up to a 49.9% reduction in CRPS and significant R² improvements.
Effective forecasting underpins grid stability, smart charging, and market optimization by aligning power demand with infrastructure capacity.

Electric Vehicle (EV) charging demand forecasting encompasses the quantitative prediction of power and energy consumption patterns at charging sites, including both public infrastructure (fast chargers, workplace/residential facilities) and home charging, over a specified horizon and spatial granularity. Precise forecasting serves as a cornerstone for grid stability, market participation, network reinforcement planning, and coordinated smart charging operations. Due to the inherent stochasticity of EV usage, temporal and spatial heterogeneity in driver behavior, and exogenous factors (weather, calendar, price), the forecasting task requires sophisticated modeling techniques that blend sequential learning, spatiotemporal dependence capture, uncertainty quantification, and practical integration with grid and market operations.

1. Problem Formalization and Challenge Dimensions

EV charging demand forecasting can be posed as multi-horizon, multi-scale time-series regression and uncertainty estimation for targets such as:

Energy Demand: Per-session or site-level kWh, aggregated at desired intervals (minutes to days)
Connection/Sojourn Duration: Session length (critical for occupancy and congestion)
Session Counts: Number of active charging events for queue management

The input space encompasses lagged demand history, temporal markers (hour, day, holiday), exogenous signals (weather, tariffs), user and station identifiers, and, for spatial models, adjacency/interaction graphs or hypergraphs. Challenges include:

Temporal Complexity: Strong periodicities (circadian/weekly), nonstationarity, regime shifts due to holidays or policy changes, error propagation in multi-step forecasts (Sanami et al., 22 Feb 2025).
Spatial Correlation: Interdependent demand spikes across adjacent or functionally similar stations necessitate explicit modeling of spatial structures (Hüttel et al., 2021, Tupayachi et al., 10 Oct 2025, Li et al., 27 Nov 2025).
Data Sparsity and Cold-Start: Many sites have limited history; effective few-shot learning and transfer across sites are crucial (Nikhal et al., 30 Oct 2025, Ali et al., 18 Sep 2024).
User/Session Heterogeneity: Disparate behavioral patterns among commuters, residents, or transient users shape load curves and impact predictability (Alikhani et al., 25 Aug 2025).
Uncertainty Requirements: Grid and market operations demand well-calibrated probabilistic outputs, not merely point forecasts (Ali et al., 18 Sep 2024, Li et al., 21 Feb 2024, Zheng et al., 1 Nov 2024).

2. Methodological Landscape

2.1 Classical and Interpretable Models

Traditional statistical paradigms include ARIMA, vector autoregression (VAR), and regression-tree ensembles such as XGBoost (Kyriakopoulos et al., 19 Dec 2025). These approaches can excel in stationary, univariate or short-term settings at the individual station scale but are generally outperformed by deep learners at larger scales and for complex multi-step tasks.

2.2 Deep Sequential Learning

The core of recent advances is deep recurrent or convolutional models, notably:

LSTM/GRU: Robust to long-horizon dependencies, capturing both trend and noise in variable-length demand sequences. Multi-variate configurations ingest demand, weather, and temporal features, often with attention layers to focus on salient history (Sanami et al., 22 Feb 2025, Ressler et al., 19 Oct 2025, Aduama et al., 2023). Bayesian LSTM variants (LSTM-BNN) enable interval prediction via dropout-driven approximation of predictive posteriors (Skala et al., 2023).
Temporal Convolutional Networks (TCN): Causal dilated convolutions provide extended receptive fields for demand, outperforming recurrent models in data-rich, high-resolution deployments (Ali et al., 18 Sep 2024).
Attention and Transformers: Transformers (self-attention-based) are the state of the art for cross-station, multi-scale operational forecasting, and particularly strong in short-term aggregation at the regional/city scale (Kyriakopoulos et al., 19 Dec 2025). Divide–Conquer Transformer architectures reduce memory bottlenecks for long-sequence, fine-grained (minute-level) home charging event prediction (Ke et al., 20 Mar 2024).

2.3 Spatiotemporal and Graph-Neural Models

Graph-based approaches are essential for leveraging inter-station dependencies:

GCN/TGCN/STGCN: Combine station adjacency graphs (physical, geographical, or functional) with node-wise time series to model spatial propagation of usage shifts (Hüttel et al., 2021, Tupayachi et al., 10 Oct 2025, Zhuang et al., 21 Aug 2024).
Federated Graph Learning: Privacy-preserving, robust frameworks distribute graph-based learners across station nodes, aggregating via attention-weighted global updates to mitigate cyberattacks and personalize predictions in heterogeneous environments (Li et al., 30 Apr 2024).
Hypergraph Models: HyperCast explicitly models groupwise, higher-order interdependencies (e.g., clusters of stations with shared temporal demand motifs) using multi-view hypergraphs, yielding substantial improvements in MAE and R² over GNN baselines (Li et al., 27 Nov 2025).

2.4 Large Language and Diffusion Models

LLM-based Models: Pretrained generative LLMs (e.g., LLAMA2-7B backbones), reprogrammed and fused with GCN-extracted features, outperform classical sequence learners on multimodal input (historical load, weather, context prompts). Partially frozen graph-attention transformers (e.g., EV-STLLM) integrate multiresolution denoising, feature selection, and domain-aware self-attention for state-of-the-art accuracy, especially under data sparsity and volatile demand (Fan et al., 4 Jun 2025, Fan et al., 13 Jul 2025).
Diffusion Models: Probabilistic models such as DiffPLF apply conditional denoising diffusion to time series, capturing the full conditional trajectory distribution and yielding well-calibrated prediction intervals under high volatility, even when exogenous disturbance factors (weather, occupancy) are strong (Li et al., 21 Feb 2024).

2.5 Ensemble and Incremental Learning

Stacked Ensembles: Layered model stacks, with meta-learners (e.g., XGBoost) over multiple base regressors, consistently yield improvement (up to +46% R² for connection duration forecasting) by capturing complementary structure. Weekly dynamic retraining supports adaptation and avoids catastrophic forgetting as user behavior drifts (Alikhani et al., 25 Aug 2025).
Transfer and Meta-Learning: Multi-quantile TCNs with parameter sharing and fine-tuning ("head-replacement") afford efficient adaptation to new sites with limited observations, maintaining high coverage probabilities in prediction intervals (Ali et al., 18 Sep 2024).

3. Feature Engineering, Data Integration, and Explainability

High-performing models employ engineered features beyond raw load:

Temporal: Periodic markers (hour, weekday, month, season, holidays, school breaks); lagged demand statistics; time-frequency decompositions (VMD, ICEEMDAN) (Alikhani et al., 25 Aug 2025, Fan et al., 13 Jul 2025).
Spatial/Functional: Station IDs, location, capacity, POI-encoded attributes; graph/hypergraph embedding based on fused geographical and demand-similarity clusters (Li et al., 27 Nov 2025).
Exogenous: Weather (temperature, humidity, precipitation), electricity prices, traffic metrics, special events; user-specific statistical summaries when historical IDs are available (Alikhani et al., 25 Aug 2025, Aduama et al., 2023).
Feature Selection: Automated routines such as ReliefF reduce redundancy in multimodal inputs (Fan et al., 13 Jul 2025). Deep models with attention or explainable AI tools (e.g., SHAP) provide post hoc variable importance, often revealing that historical demand and calendar/weather variables dominate predictions (Sanami et al., 22 Feb 2025).

4. Empirical Performance and Comparative Benchmarks

Rigorous, cross-city evaluation consistently demonstrates:

Transformers and LSTM/GRU outperform ARIMA/XGBoost for mid/long-term (hourly/daily), regional/metropolitan forecasting (Kyriakopoulos et al., 19 Dec 2025).
Ensembles (stacking, few-shot meta-learners) outperform any single model, especially under strong intra-dataset variability or as more candidate regressors are included (Alikhani et al., 25 Aug 2025, Nikhal et al., 30 Oct 2025).
Graph-augmented, spatiotemporal architectures (TGCN, GCN+1D-CNN, HyperCast) reduce error over both CNN/LSTM and classical GCNs by exploiting spatial and demand-driven clustering (Hüttel et al., 2021, Li et al., 27 Nov 2025).
Probabilistic, quantile/TQN and diffusion models provide more sharply calibrated and coherent prediction intervals compared to quantile regression, pinball loss minimizers, and naive variance models, with up to 49.9% reduction in CRPS (Li et al., 21 Feb 2024, Ali et al., 18 Sep 2024, Zheng et al., 1 Nov 2024).
Explicit model adaptation for new/low-data sites (transfer learning, clustering, or dual-model approaches) is essential for robust deployment as infrastructure expands rapidly to new locations (Ali et al., 18 Sep 2024, Nikhal et al., 30 Oct 2025).

Typical reported metrics:

Model/Method	MAE	RMSE	R²	PICP	Coverage Interval
LSTM+Attention	0.0680	0.0922	—	—	—
Stacking Ensemble	—	—	up to 0.83	—	—
1D-CNN+GCN (3-h)	0.064	0.528	0.9659	—	—
DiffPLF	7.16	—	—	—	CRPS=5.07
MQ-TCN (TL)	—	—	—	96.88%	90%
HyperCast	14.8-21.3	—	0.80-0.89	—	—

5. Uncertainty Quantification and Decision Support

Practical adoption requires probabilistic forecasting:

Quantile regression, pinball loss, and multi-head outputs: Allow construction of empirical prediction intervals for each horizon step with coverage guarantees (Ali et al., 18 Sep 2024, Zheng et al., 1 Nov 2024).
Gaussian mixture model error-fitting: Used in sequential forecast-then-optimize pipelines for real-time operational risk assessment (e.g., grid hosting capacity computation) (Zhuang et al., 21 Aug 2024).
Conformal prediction: Proposed as a modular overlay for calibrated, distribution-free uncertainty intervals (Alikhani et al., 25 Aug 2025).
Scenario reconciliation with hierarchical constraints: Differentiable convex optimization post-processing (DCLs) enforces coherency between site-level and aggregate forecasts, optimizing scenario sharpness and aggregation properties (Zheng et al., 1 Nov 2024).

These interval outputs inform:

Smart charging: Real-time/rolling scheduling to align flexible demand with grid objectives and capacity constraints, under explicit uncertainty (Alikhani et al., 25 Aug 2025).
Grid integration: Forecasts and intervals underpin dynamic transformer sizing, demand response trigger policies, and market/ancillary service bidding (Ali et al., 18 Sep 2024, Zhuang et al., 21 Aug 2024).
Dynamic pricing and load balancing: Downstream integration with RL agents for network-wide equilibrium between user satisfaction and system cost, via price- or incentive-driven demand shifts (Mosalli et al., 9 Mar 2025).

6. Deployment, Adaptation, and Future Directions

Best practices for operationalization highlighted by the literature include:

Continuously updated/incrementally trained models to capture evolving patterns, especially critical in environments with dynamic policy, user composition, or event-induced disruptions (Alikhani et al., 25 Aug 2025, Ressler et al., 19 Oct 2025).
Integration of explainable model outputs (e.g., SHAP attributions, attention heatmaps) in station/operator dashboards for real-time decision support and scenario analysis (Sanami et al., 22 Feb 2025).
Transferable and few-shot learners for rapid deployment at new sites with limited or no station-specific history, delivering high reliability with minimal data (Ali et al., 18 Sep 2024, Nikhal et al., 30 Oct 2025).
Multi-scale, multi-view integration: Hypergradient models and LLM hybrids capture latent groupwise structure and nonlocal dependencies, improving robustness on complex urban topologies (Li et al., 27 Nov 2025, Fan et al., 4 Jun 2025, Fan et al., 13 Jul 2025).
Privacy and security: Federated learning and robust aggregation are essential to counter adversarial attacks and preserve data confidentiality in large-scale multi-owner networks (Li et al., 30 Apr 2024).

Emergent research directions include dynamic graph/hypergraph learning to incorporate evolving infrastructure, close integration between load forecasting and charging/station scheduling optimization, and the joint modeling of EV, renewable, and demand-response dynamics for holistic grid planning.

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