Energy Demand Prediction with Federated Learning for Electric Vehicle Networks

Diep N. Nguyen; Dinh Thai Hoang; Eryk Dutkiewicz; Markus Dominik Mueck; Srikathyayani Srikanteswara; Yuris Mulya Saputra

REVIEW 4 major objections 5 minor 1 cited by

Energy Demand Prediction with Federated Learning for Electric Vehicle Networks

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Federated learning cuts EV charging forecast error by 24.63%

desk verdict An honest incremental federated-learning benchmark for EV charging demand whose qualitative claims hold but whose headline percentages and overhead metric are not reproducible from the presented data. read the letter →

arxiv 1909.00907 v1 pith:BEYIU2AL submitted 2019-09-03 eess.SP cs.LG

Yuris Mulya Saputra , Dinh Thai Hoang , Diep N. Nguyen , Eryk Dutkiewicz , Markus Dominik Mueck , Srikathyayani Srikanteswara This is my paper · ORCID

classification eess.SPcs.LG

keywords energydemandpredictionelectricvehicleschargingstationsfederatedlearningclusteringdeepcommunicationoverheadprivacy

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that federated deep learning can predict the electricity demand of electric-vehicle charging stations as accurately as a central model that gathers all data, while sending only model updates over the network. On a real dataset of 65,601 charging sessions in Dundee, UK, the proposed methods reduced prediction error (RMSE) by up to 24.63% relative to six conventional machine-learning baselines, and cut communication overhead by 83.4%. This matters because charging-station providers need accurate forecasts to reserve energy in advance and avoid grid congestion, and because privacy concerns can block central collection of driving and charging data.

What carries the argument

The machinery is a multilayer perceptron with two hidden layers of 64 neurons each, tanh activation, and a dropout layer, trained with the Adam optimizer. The federated variant adds a synchronous averaging step that combines local gradients from charging stations into a global model, while the clustering variant wraps the learner in a constrained K-means algorithm that partitions stations by latitude and longitude so that each cluster learns its own model. The paper's quantitative claims rest on these components working together on the Dundee charging-session dataset.

What would settle it

A reader could re-run the FEDL algorithm on the Dundee dataset or a similar one, but with an artificially unbalanced split (e.g., one station holds 80% of transactions) and compare the RMSE against a centralized model trained on the same data; if the federated model's error exceeds the centralized model's error by more than the claimed 24.63% margin, the synchronous-averaging assumption fails in that regime.

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Extended reading notes

Core claim

The central discovery is that federated energy demand learning (FEDL) — where each charging station trains a local deep neural network and sends only its gradients to a central provider that averages them — can outperform both conventional regressors and a fully centralized deep network in forecasting transaction-level energy demand. Adding a location-based constrained K-means clustering step before learning further reduces error by grouping stations with similar features, and the federated design sends no raw data, shrinking communication overhead by 83.4% compared to central collection.

Load-bearing premise

The method assumes that averaging gradients from charging stations with very different numbers of transactions and demand patterns produces a global model that predicts as well as a model trained directly on all the data together.

Editorial extensions

If this is right

Charging-station providers can offer real-time demand forecasts without collecting raw transaction logs, preserving user privacy.
Communication load drops to roughly one-sixth of centralised collection, enabling frequent model updates even over bandwidth-limited links.
Location-based clustering is a cheap way to reduce bias when demand patterns differ by area, and it can be layered on any federated or central learner.
The same pipeline could be applied to other sparse, spatio-temporal demand forecasting problems such as bike-sharing or warehouse energy use.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The experiments use a single city dataset with 58 stations, so the claimed gains may not transfer to networks where stations have highly skewed transaction counts, where the synchronous aggregation could slow or bias convergence.
A natural testable extension is to compare FEDL against a centrally trained model on the same data with a non-IID split that mimics real-world station heterogeneity; the paper does not report that baseline.
The privacy argument rests on gradients being hard to reverse-engineer; for small batches, gradient leakage is a known risk the paper does not address.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

Quick take: this is a sincere, incremental applied ML paper that does one new thing—benchmarks federated and clustered DNNs against six standard regressors on the public Dundee EV charging dataset—and the central qualitative claim holds. But the headline numbers do not come out of the paper’s own table, and the communication-overhead claim is undefined. Fix those and it becomes a reasonable applied paper.

What’s actually new: not federated averaging or constrained k-means; both are established. The contribution is the combination and the evaluation on real charging-session data (65,601 transactions), including the clustered variants (K=2) that group stations by location before training. That specific comparison is not in the cited prior work. The paper lays out the centralized/federated/clustered design space clearly, and the algorithms are easy to follow.

What it does well: the problem is real, the privacy motivation for federated learning is sensible for charging-station providers, and Table I consistently shows the proposed methods below all six baselines across four training splits. So the central direction—federated and clustered deep learning beat classical regressors here—is supported. The citations look appropriate; I don’t see any missing-key-work or citation-stacking red flags.

Soft spots, in order of severity. (1) The abstract’s “up to 24.63%” and Section IV-C’s “24.28% and 24.63%” do not match Table I under any standard formula. At 80% training, the best single-baseline comparison gives about 9.3% vs RF and 19.8% vs KNR; the largest cross-split improvement is about 25.0% at 70%. The paper never defines the improvement formula. That is a real reproducibility gap in the primary advertised result. (2) No error bars, seeds, or cross-validation. The differences among EDL, FEDL, and their clustered variants are roughly 0.1 kWh (1–2%), so the claimed advantage of clustering over plain EDL/FEDL is not statistically supported. (3) Figure 4’s “communication overhead (MB)” is undefined—data volume? number of messages?—so the 83.4% reduction is unverifiable. (4) One dataset, one clustering K, and several free hyperparameters; the non-IID concern about synchronous gradient averaging is plausible but not analyzed.

None of these sink the paper. They are presentation and evidence-standards issues in an otherwise straightforward empirical study.

Who this is for: people working on EV charging prediction or privacy-preserving demand forecasting who want a baseline comparison and a manageable federated-learning application. It does not change the science, but it is a useful data point. I would give it a serious referee, with a clear request to fix the percentage arithmetic, define the communication metric, and add uncertainty estimates or code.

Recommendation: send to peer review, expect revision, and make the authors reconcile the abstract with Table I before acceptance.

Referee Report

4 major / 5 minor

Summary. The paper proposes three approaches for predicting EV charging-station energy demand: a centralized energy demand learning (EDL) method, a federated energy demand learning (FEDL) method in which charging stations share only local gradients with a charging station provider, and clustering-based variants of both. The methods are evaluated on a public dataset of 65,601 charging transactions from Dundee, UK, and compared with six baseline regressors (KNR, MLPR, SGDR, DT, SVR, RF) at training splits of 80%, 70%, 60%, and 50%. The abstract reports accuracy improvements up to 24.63% and communication-overhead reductions of 83.4% relative to the baselines. The paper also describes a customized constrained K-means clustering algorithm that groups charging stations by location while enforcing cluster-size bounds.

Significance. If the empirical claims are reproducible, the paper makes a useful contribution: it demonstrates that a federated-learning approach can provide competitive energy-demand prediction accuracy while avoiding raw data collection, which addresses both communication cost and privacy in EV networks. The use of a public real-world dataset and the inclusion of six independent baseline algorithms are strengths, and the clustering formulation with explicit size constraints is a reasonable design choice. However, the headline quantitative claims are not currently verifiable from the manuscript: the 24.63% accuracy improvement does not match any standard formula applied to Table I, and the 83.4% communication-overhead reduction is supported only by an undefined figure. The absence of error bars or repeated-trial statistics also leaves open whether the small differences among EDL, FEDL, and their clustered variants are meaningful. These issues are central because the paper's advertised contributions are precisely these numerical improvements.

major comments (4)

[§IV-C, Table I] The headline claim of up to 24.63% accuracy improvement is not reproducible from Table I under any straightforward formula. For the 80% split, the best baseline (RF) has RMSE 6.35 and FEDL+Clustering has RMSE 5.76, giving an improvement of (6.35−5.76)/6.35 ≈ 9.3%; against the worst baseline (KNR, 7.18) the improvement is ≈ 19.8%. The only value close to 24.63% is at the 70% split using the worst baseline: (7.71−5.78)/7.71 ≈ 25.0%, which is not 24.63%. The text in Section IV-C states 24.28% and 24.63% for the 80% split without defining the baseline or the percentage formula. Please provide the exact computation, either as per-baseline percentages or with an explicit formula such as (RMSE_baseline − RMSE_proposed)/RMSE_baseline, and report the corresponding values for each training split.
[Fig. 4] The communication-overhead reduction of 83.4% is not supported by the presented evidence. Fig. 4 shows bars on an axis labeled 'Communication overhead (MB)' but the manuscript does not define what is being measured (e.g., total bytes of raw data versus model/gradient updates, number of messages, or per-round traffic), what the baseline value is, how the baseline is computed for methods that are not centralized (e.g., KNR, which may not involve data transmission at all), or how the 83.4% figure is derived. Without these definitions and a numerical calculation, the overhead claim cannot be checked. Please specify the measurement protocol, report the underlying quantities, and show the arithmetic for the reduction.
[§IV-C, Table I] The reported RMSE differences among EDL, FEDL, EDL+Clustering, and FEDL+Clustering are very small (e.g., 5.86 vs. 5.81 vs. 5.77 vs. 5.76 at the 80% split, a range of 0.10 kWh, or about 1.7% relative). The paper provides no error bars, number of random seeds, cross-validation, or statistical significance tests. As a result, the claims that clustering 'further improves prediction accuracy' and that FEDL 'achieves the average prediction with less variance and lower error' are not substantiated: the observed differences could easily arise from training noise. Please report repeated runs with different seeds and, if possible, confidence intervals or pairwise significance tests.
[§III-B, Eq. (12)] The FEDL method rests on the assumption that synchronously averaging local gradients, as in Eq. (12), yields a global model with accuracy comparable to centralized training. This assumption is known to be fragile when local datasets are highly non-IID, and the paper does not characterize the distribution of transactions across the charging stations or provide any convergence analysis. Since the accuracy numbers in Table I are based on a specific (and unspecified) partition of the data into J subsets, it is unclear whether the claimed 1–2% gap between FEDL and EDL would persist under more heterogeneous partitions. Please report the distribution of transactions per station, describe how the J training subsets were created, and discuss or experimentally test the effect of non-IID data on FEDL convergence.

minor comments (5)

[Eq. (12)] In Eq. (12), the summand is written as ∇υ(φ)_i but the summation index is j; this should be ∇υ(φ)_j.
[Algorithm 1] The notation 'em_i' in Algorithm 1 is not defined anywhere; please explain what it represents (presumably a per-transaction error term).
[§IV-B] The experimental setup does not state the values of J, K, θlow, θhigh, the number of epochs T, or the dropout rate's relationship to the hidden-layer sizes. These hyperparameters are needed to reproduce the experiments.
[§IV-C] The sentence 'the RMSE obtained by the centralized EDL with clustering and the FEDL with clustering are 24.28% and 24.63% lower than those of the baseline methods' is ambiguous: it does not say which baseline or baselines are used. Please clarify.
[Fig. 4] The figure would be much more informative if the numerical values were printed on the bars and if the baseline category were disaggregated (e.g., showing the overhead for each centralized method).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the accuracy and overhead claims are empirical evaluations against external baselines on a public dataset, not derivations that bake in their conclusions.

full rationale

The paper's central claims (up to 24.63% RMSE improvement and 83.4% communication-overhead reduction) are presented as experimental outcomes, not as theorems derived from assumptions that already contain the conclusions. The accuracy comparison is made against six independently implemented baseline regressors (DT, RF, SVR, KNR, SGDR, MLPR) on the public Dundee City charging-session dataset, with RMSE computed by Eq. (20) on held-out test portions for several training ratios. No parameter of the proposed EDL/FEDL/clustering algorithms is fitted to the reported improvement value, and no equation in Sections III-A through III-C reduces to the target metric by construction. The 83.4% overhead figure is a measured consequence of the FEDL protocol's design (transmitting gradients rather than raw datasets) rather than a prediction that is equivalent to its input. The paper contains no load-bearing self-citations: reference [16] is external work on distributed deep networks and [17] is the external constrained K-means source. The concern that the 24.63% headline is not directly reproducible from Table I (for example, the 80% split yields roughly 9% over the best baseline and about 20% over the worst baseline) and that Fig. 4 lacks units and a measurement protocol is a verifiability and reporting issue, not circularity; therefore it does not raise the circularity score.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new mathematical objects. Its central empirical claims rest on hyperparameters chosen by hand and on standard domain assumptions about the dataset, the neural network architecture, and the convergence of gradient averaging. None of these are externally validated inside the paper, so they are counted here as assumptions rather than results.

free parameters (6)

DNN architecture = 2 hidden layers, 64 neurons each
Chosen by hand in Section IV-B; no architecture search or validation reported.
Dropout rate = 0.15
Chosen in Section IV-B to avoid overfitting; no sensitivity analysis.
Adam initial learning rate = 0.01
Chosen in Section IV-B; standard value, not tuned.
Number of clusters K = 2
Chosen in Section IV-B; no sensitivity analysis, though clustering benefit depends on K.
Cluster size bounds theta_low and theta_high = not specified
Constraints in Eq. (14) are invoked but their values are never given.
Federated worker count J and local epochs = not specified
Section IV-B says the training set is divided into J subsets, but J and local epoch counts are unstated; the synchronized averaging result depends on these.

assumptions (4)

domain assumption The Dundee charging session dataset is representative and reliable.
Section IV-A uses 65,601 records from 58 stations as ground truth; no data quality or annotation error analysis is provided.
domain assumption A two-hidden-layer tanh network with the stated preprocessing can approximate the demand function well enough to improve on baselines.
Section III and IV-B; no validation that this architecture is sufficient for other cities or time periods.
domain assumption Synchronous averaging of local gradients in Eq. (12) yields a global model comparable to centralized training, even when local station datasets differ in size and demand patterns.
Section III-B assumes gradient staleness is zero with synchronous aggregation; non-IID effects are not tested.
standard math The Adam optimizer update rules in Eqs. (5)-(7) converge to a low-error model.
Applied without proof in Section III-A; standard in the deep learning literature.

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Cite this review

Pith. "Pith review of Energy Demand Prediction with Federated Learning for Electric Vehicle Networks." pith.science (2026). https://pith.science/paper/BEYIU2AL

@misc{pith2026190900907,
  author       = {Pith},
  title        = {Pith review of: Energy Demand Prediction with Federated Learning for Electric Vehicle Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BEYIU2AL}},
  note         = {Machine review of arXiv:1909.00907}
}

read the original abstract

In this paper, we propose novel approaches using state-of-the-art machine learning techniques, aiming at predicting energy demand for electric vehicle (EV) networks. These methods can learn and find the correlation of complex hidden features to improve the prediction accuracy. First, we propose an energy demand learning (EDL)-based prediction solution in which a charging station provider (CSP) gathers information from all charging stations (CSs) and then performs the EDL algorithm to predict the energy demand for the considered area. However, this approach requires frequent data sharing between the CSs and the CSP, thereby driving communication overhead and privacy issues for the EVs and CSs. To address this problem, we propose a federated energy demand learning (FEDL) approach which allows the CSs sharing their information without revealing real datasets. Specifically, the CSs only need to send their trained models to the CSP for processing. In this case, we can significantly reduce the communication overhead and effectively protect data privacy for the EV users. To further improve the effectiveness of the FEDL, we then introduce a novel clustering-based EDL approach for EV networks by grouping the CSs into clusters before applying the EDL algorithms. Through experimental results, we show that our proposed approaches can improve the accuracy of energy demand prediction up to 24.63% and decrease communication overhead by 83.4% compared with other baseline machine learning algorithms.

Figures

Figures reproduced from arXiv: 1909.00907 by the authors.

**Figure 2.** Federated energy demand learning architecture. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Charging station clustering. the upcoming energy demand prediction. For clustering, we define K = 2, and thus we divide 58 CSs into 2 clusters as shown in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Communication overhead between baseline and pro [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting
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A federated learning framework that clusters clients by static features before training reaches near-centralized photovoltaic forecasting accuracy with minimal degradation on new sites.

Reference graph

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

Available Online: https://www.cnbc

The Growth of Electric Vehicles. Available Online: https://www.cnbc. com/2018/05/30/electric-vehicles-will-grow-from-3-million-to-125- million-by-2030-iea.html. Last Accessed on April 2019

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work page 2011

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P. You and Z. Yang, “Efﬁcient optimal scheduling of charging station with multiple electric vehicles via V2V ,” in IEEE SmartGridComm , Nov. 2014, pp. 716721

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M. Majidpour, et al. , “Fast prediction for sparse time series: demand forecast of EV charging stations for cell phone applications,” IEEE Transactions on Industrial Informatics , vol. 11, no. 1, pp. 242-250, Feb. 2015

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Reinforcement learning-based plug-in electric vehicle charging with forecasted price,

A. Chis, J. Lunden, and V . Koivunen, “Reinforcement learning-based plug-in electric vehicle charging with forecasted price,” IEEE Trans- actions on V ehicular Technology, vol. 66, no. 5, pp. 3674-3684, May 2017

work page 2017

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A. Fukushima, et al. , “Prediction of energy consumption for new electric vehicle models by machine learning,” IET Intelligent Transport Systems, vol. 12, no. 9, pp. 1174-1180, Oct. 2018

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Distributed charging control of elec- tric vehicles using online learning,

W. Ma, V . Gupta, and U. Topcu, “Distributed charging control of elec- tric vehicles using online learning,” IEEE Transactions on Automatic Control, vol. 62, no. 10, pp. 5289-5295, Oct. 2017

work page 2017