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REVIEW 3 major objections 7 minor 1 cited by

Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders

T0 review · 3 major / 7 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A variational autoencoder imputes missing installation ages of medium-voltage cables with accuracy on par with KNN, MissForest, and iterative imputation.

desk verdict Honest proof-of-concept for VAE imputation on Danish MV cable data, but the benchmark doesn't yet support deployment under MNAR. read the letter →

arxiv 2501.10920 v1 pith:7FQ45DT5 submitted 2025-01-19 cs.LG cs.SYeess.SY

Konrad Sundsgaard , Kutay Bölat , Guangya Yang This is my paper · ORCID
classification cs.LGcs.SYeess.SY
keywords variationalautoencodersdataimputationdistributiongridcablesmedium-voltagesyntheticgenerationmissingreliabilitymodelingassetregister
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

Distribution-grid asset registers in Denmark often lack the installation date of medium-voltage cables, which blocks age-based reliability modeling. This paper argues that a variational autoencoder (VAE) can fill that gap: it trains a VAE on cable features, amputates known ages to test imputation, and finds the model outperforms random and mean-based imputation while matching the accuracy of KNN, iterative imputation, and MissForest. The same model also generates synthetic cable records whose marginal distributions closely track the original data, pointing to a single tool for imputation, data balancing, and privacy-preserving data sharing. The study is explicitly a proof of concept, with the authors noting that real missingness may be biased toward older cables.

What carries the argument

The central object is the variational autoencoder, a generative neural network that encodes data into a low-dimensional latent space and decodes samples from a prior back into data space; its objective here is a weighted sum of a continuous negative log-likelihood loss, a categorical cross-entropy loss, and a KL-divergence regularizer. For imputation, the trained model is used with pseudo-Gibbs sampling, which starts from an initial guess for the missing age and iteratively refines it through repeated encoder–decoder passes. This mechanism lets the same model both generate synthetic cable records and fill in missing values, with the conditioning extension (concatenating extra observed variables) available for guided generation.

What would settle it

Re-run the imputation benchmark using amputation patterns that mimic missing-not-at-random data, for example removing ages preferentially for older cables, and compare the VAE's MAE, RMSE, and R2 against KNN and MissForest; if the VAE's accuracy degrades below those baselines, the proof of concept does not transfer to the real asset registers.

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Extended reading notes

Core claim

The central claim is that VAEs are a suitable foundation for data enrichment in medium-voltage cable reliability studies, demonstrated by imputing the missing installation age of Danish cable sections. In amputation tests, the VAE ranks best among KNN, Iterative Imputer (IIm), MissForest, and itself, although the margin over the other sophisticated methods is not significant. Validated by Kolmogorov-Smirnov statistics around 0.06–0.13, samples drawn from the trained VAE reproduce the marginal distributions of the original age and length features, and the authors use this to argue that generative models offer imputation and synthetic-data generation in one framework. The proof of concept does not claim superiority in accuracy alone; its case rests on matching state-of-the-art imputation while adding capabilities those methods lack.

Load-bearing premise

The evaluation assumes that randomly amputating known cable ages is a faithful stand-in for real missingness, although the paper acknowledges that missing records are likely biased toward older cables.

Editorial extensions

If this is right

  • A single trained VAE can replace separate pipelines for imputation and synthetic-data generation in MV cable reliability studies.
  • Synthetic records that match marginal distributions of real asset data could let DSOs share richer datasets without releasing sensitive individual records.
  • Since imputation accuracy is only competitive, the practical case for adopting a VAE depends on its auxiliary benefits rather than on beating MissForest or KNN.
  • Closing the accuracy gap will require adding external features such as network topology, weather, and digging registers rather than further tuning the VAE architecture.

Reading between the lines

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

  • One untested but plausible extension is that VAEs will degrade less than KNN or MissForest under MNAR missingness, because the latent distribution can encode dependencies that tabular imputers miss; an amputation study biased toward older cables could test this directly.
  • The distribution-matching result suggests the same architecture may transfer to low-voltage cable registers, where records are far more numerous but of lower quality; a scaled-up case study would show whether the VAE advantage grows with data volume.
  • If synthetic cable data prove sufficient for downstream reliability models, the VAE could act as a privacy-preserving data-sharing layer between utilities, a consequence the authors flag only as a future need.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 7 minor

Summary. This manuscript presents a proof-of-concept study of VAE-based data enrichment for Danish medium-voltage cable asset registers, with the main application being imputation of missing cable installation ages. The authors train a VAE on complete-case asset records, evaluate synthetic-data fidelity with ECDF plots and KS statistics, and benchmark imputation quality from random amputation against random/mean/median/mode imputation as well as KNN, Iterative Imputer, and MissForest. They conclude in Section VI that the VAE 'demonstrates competitive performance' and can perform targeted imputations and create enriched datasets, while acknowledging in Section V that real missingness is likely MNAR and that real-data imputation remains future work.

Significance. The application domain is relevant, and the benchmark design is a reasonable first step: comparing the VAE against KNN, Iterative Imputer, and MissForest is appropriate, and the authors report key hyperparameters and use MLflow for experiment tracking. The paper's clearest strength is its explicit, hedged discussion of limitations, including the MNAR concern in Section V, which shows appropriate caution. If the amputation results were backed by uncertainty quantification and an MNAR robustness check, the proof-of-concept claim would be credible and useful for DSO-facing data-enrichment work. The methodological contribution is incremental, but the domain application is non-trivial. I found no circularity: the VAE is trained on complete-case data and evaluated on held-out amputated records, and the hyperparameters are not selected through imputation accuracy.

major comments (3)
  1. [§IV-C3, §V, §VI] The central quantitative evidence for the conclusion that the VAE 'can perform targeted imputations' comes from the random amputation experiment in §IV-C3. Section V explicitly states that in the real asset register 'missingness is likely biased toward older cables, suggesting MNAR.' Random amputation simulates MCAR/MAR, so the complete-case validation records are representative of younger, documented cables rather than the older cables whose ages are actually missing. The measured MAE, RMSE, and R² therefore need not transfer to the deployment setting, and the Section VI claim is not yet supported. Please add an MNAR-style amputation (for example, removal probability increasing with true age) or validate on a genuinely partially observed register, and report how performance varies with the assumed missingness mechanism.
  2. [§IV-C3, Figures 7–8] The statement in §IV-C3 that the VAE 'does not significantly outperform' the other imputation methods is not backed by any statistical test or uncertainty quantification. Figures 7 and 8 report single point estimates without error bars, confidence intervals, or repeated amputation trials. Because the paper's headline is 'competitive performance,' this is load-bearing: the authors should run multiple amputation seeds, report distributions or confidence intervals over the metrics, and apply a paired significance test (e.g., Wilcoxon signed-rank) between the VAE and KNN, Iterative Imputer, and MissForest.
  3. [§IV-C1, Figure 3, Table II] The VAE was trained with a constrained hyperparameter search and the text notes that training was 'terminated prior to achieving complete convergence.' Since the VAE is compared against classical imputation baselines, the observed ranking may depend on the stopping point and on the chosen α and β rather than on the model class. Please either train to convergence, show that the imputation metrics are stable over the final training epochs, or report a small sensitivity analysis over α and β so that the comparison reflects the VAE's capability.
minor comments (7)
  1. [§II] The text 'as Fig. ?? underlines' contains an unresolved cross-reference; it should point to Figure 1.
  2. [§IV-C3] The phrase 'systematically removing a substation portion of the data' appears to be a typo for 'substantial portion,' and 'installation data' should read 'installation dates' or 'installation age' for precision.
  3. [Figure 8] The legend 'ITer' is inconsistent with the text's 'IIm' for Iterative Imputer; please standardize the abbreviation.
  4. [§III-D] The text contains a typo ('seme-supervised') and the described semi-supervised strategy is not actually implemented in the case study; please clarify that the experiments use the unsupervised VAE, not the semi-supervised variant.
  5. [Table III] The KS statistics and synthetic-data moments are reported without sample size or confidence intervals; adding N and a note on whether the KS test accounts for the synthetic-sample size would make the distributional comparison more interpretable.
  6. [Abstract and Keywords] The abstract lists imbalanced-data handling and outlier detection as investigated capabilities, but the case study provides no experiments for these; please either add such analyses or soften the wording. Similarly, the keyword 'conditional variational autoencoders' overstates the implemented model, since the case study uses an unconditional VAE.
  7. [§IV-C3] Please report the dataset size, number of complete-case records, amputation fraction, and train/validation split used for Figures 7 and 8; this information is essential for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: VAE imputation is benchmarked on amputated held-out data against external imputation baselines, and no target result is defined by the fitted constants.

full rationale

The paper's central quantitative claim is that a VAE imputes missing cable installation ages with competitive performance. This is supported by amputation testing in Section IV-C3, where known ages are removed from a validation set and imputed, with MAE, RMSE, and R2 computed against the ground truth and compared with KNN, Iterative Imputer, and MissForest. The VAE is trained on complete-case asset data using a standard VAE loss (Eq. 1); the imputation error is not used to define any model parameter or hyperparameter, and the baselines are external methods. The real-versus-synthetic distribution comparison in Section IV-C2 and Table III is an in-sample fit check of the generative model, not a prediction derived from a fitted constant, and it is presented as a sanity check rather than as evidence of imputation skill. The Section V concession that real missingness is likely MNAR and that amputation should be replaced by real imputation is a limitation on external validity, not a circular step. Self-citations such as [5], [6], and [8] support background statements about data scarcity and prior text-based methods; they do not carry the VAE imputation result. The lack of error bars or significance tests in Figures 7 and 8 is a statistical robustness concern, but it is not circularity. No step in the paper reduces, by its own equations or by self-citation, to its own inputs.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central imputation result rests on two domain assumptions explicit in the text: complete-case representativeness and feature-age dependence. The loss function adds a modeling assumption about Gaussian and categorical likelihoods. No new entities are introduced. Hyperparameters are tuned on the data but are standard model choices rather than a derived law.

free parameters (3)
  • alpha (continuous/categorical loss weight) = 0.07127
    Tuned during constrained hyperparameter optimization; balances the continuous and categorical reconstruction losses in Eq. (1).
  • beta (KL divergence weight) = 0.0275
    Scales the KL regularization term in Eq. (1); chosen by hyperparameter search.
  • architecture hyperparameters = hidden dim 145, latent dim 13, batch size 128, learning rate 0.0001
    Model capacity and training settings listed in Table II; selected during constrained optimization and affect imputation quality.
assumptions (4)
  • domain assumption Complete-case records with known installation age are representative of all cable sections, so a model trained on them can impute missing ages accurately.
    Amputation testing in Section IV-C3 removes ages at random from the validation set; the paper later notes real missingness is likely MNAR biased toward older cables, which violates this assumption.
  • domain assumption Installation age is statistically dependent on the available asset features such as length, voltage, DSO, insulation, conductor material and size, and number of conductors.
    The imputation pipeline assumes these features carry signal about age; the modest R2 values in Fig. 8 suggest the dependence is weak.
  • domain assumption The chosen VAE likelihood, unit-variance Gaussian for continuous features and cross-entropy for categorical features with KL regularization, adequately models the mixed-type asset data.
    The loss function in Section IV-B imposes this likelihood; the ECDF comparisons in Section IV-C2 are visual and not a formal goodness-of-fit test.
  • standard math VAE training via the evidence lower bound and reparameterization is a valid way to approximate the data distribution.
    Standard result from Kingma and Welling and Rezende et al., used throughout Section III.

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Cite this review

Pith. "Pith review of Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders." pith.science (2026). https://pith.science/paper/7FQ45DT5

@misc{pith2026250110920,
  author       = {Pith},
  title        = {Pith review of: Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7FQ45DT5}},
  note         = {Machine review of arXiv:2501.10920}
}
read the original abstract

Electricity distribution cable networks suffer from incomplete and unbalanced data, hindering the effectiveness of machine learning models for predictive maintenance and reliability evaluation. Features such as the installation date of the cables are frequently missing. To address data scarcity, this study investigates the application of Variational Autoencoders (VAEs) for data enrichment, synthetic data generation, imbalanced data handling, and outlier detection. Based on a proof-of-concept case study for Denmark, targeting the imputation of missing age information in cable network asset registers, the analysis underlines the potential of generative models to support data-driven maintenance. However, the study also highlights several areas for improvement, including enhanced feature importance analysis, incorporating network characteristics and external features, and handling biases in missing data. Future initiatives should expand the application of VAEs by incorporating semi-supervised learning, advanced sampling techniques, and additional distribution grid elements, including low-voltage networks, into the analysis.

Figures

Figures reproduced from arXiv: 2501.10920 by the authors.

Figure 1
Figure 1. Data availability for selected features in failure reports, sorted by [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Computational diagram of a conditional CVAE. Embedding layers are applied to categorical inputs and conditions. [13] [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Training and Validation Loss of the VAE (asset data) over different [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 6
Figure 6. Figure 6: Empirical Cumulative Distribution Function (ECDF) for the categor [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 4
Figure 4. Figure 4: Empirical Cumulative Distribution Function (ECDF) for the continu [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Empirical Cumulative Distribution Function (ECDF) for the continu [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 7
Figure 7. Figure 7: Benchmarking the metrics of VAE’s imputation performance against simpler imputation methods such as random and informed random imputation. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Benchmarking the metrics of VAE’s imputation performance against more sophisticated state-of-the-art imputation methods such as KNN, ITer, and [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

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Cited by 1 Pith paper

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Reference graph

Works this paper leans on

16 extracted references · 13 canonical work pages · cited by 1 Pith paper

  1. [1]

    Machine learning methods for the health-indexing and ranking of underground distribution cables and joints,

    J. Heres, R. Stijl, and F. Reinders, “Machine learning methods for the health-indexing and ranking of underground distribution cables and joints,” in CIRED Workshop 2016 , 2016, pp. 1–4

    work page 2016

  2. [2]

    Big data applications for improving the reliability of the french electricity distribution grid,

    J. Merigeault and O. Faivre, “Big data applications for improving the reliability of the french electricity distribution grid,” pp. 1–33, 2021. [Online]. Available: https://doi.org/10.1007/978-3-030-72322-4 33-1

    work page doi:10.1007/978-3-030-72322-4 2021

  3. [3]

    Designing digitally enabled proactive maintenance systems in power distribution grids: A scoping literature review,

    L. K. Mortensen, K. Sundsgaard, H. R. Shaker, J. Z. Hansen, and G. Yang, “Designing digitally enabled proactive maintenance systems in power distribution grids: A scoping literature review,” Energy Reports , vol. 12, pp. 1–21, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2352484724005353

    work page 2024

  4. [4]

    Failure statistic for medium voltage cable systems in denmark,

    J. Z. Hansen, “Failure statistic for medium voltage cable systems in denmark,” in Proceedings of the CIRED 2023 Conference . Rome, Italy: CIRED, 2023

    work page 2023

  5. [5]

    Rethinking data require- ments for the reliability assessment of medium voltage cables,

    K. Sundsgaard, J. Z. Hansen, and G. Yang, “Rethinking data require- ments for the reliability assessment of medium voltage cables,” in Conference Proceedings of CIRED 2023 International Conference & Exhibition on Electricity Distribution , 2023

    work page 2023

  6. [6]

    Exploring data collection and fusion of medium voltage cable failures - a danish case study,

    K. Sundsgaard, J. Z. Hansen, G. Yang, M. Cafaro, and P. K. Hansen, “Exploring data collection and fusion of medium voltage cable failures - a danish case study,” in 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) , 2023, pp. 1–5

    work page 2023

  7. [7]

    A decentralised relational data model for reli- ability studies of medium-voltage cables,

    K. Sundsgaard et al. , “A decentralised relational data model for reli- ability studies of medium-voltage cables,” 2024 IEEE PES Innovative Smart Grid Technologies - Europe (ISGT Europe) , 2024

    work page 2024

  8. [8]

    Enhancing distribution grid failure data by leveraging ai-driven text classification: A danish case study,

    ——, “Enhancing distribution grid failure data by leveraging ai-driven text classification: A danish case study,” CIGRE 2024 Paris Session , 2024

    work page 2024

Show all 16 references
  1. [9]

    Optimizing network replacement with ai,

    O. Faivre, P. Cochet, J. M ´erigeault, and S. Folleville, “Optimizing network replacement with ai,” in CIRED 2019 Conference . Madrid, Spain: AIM, June 2019. [Online]. Available: https://cired-repository. org/handle/20.500.12455/545

    2019

  2. [10]

    Auto-encoding variational bayes,

    D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114, 2013

    2013 arXiv

  3. [11]

    Stochastic backpropaga- tion and approximate inference in deep generative models,

    D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropaga- tion and approximate inference in deep generative models,” 2014

    2014

  4. [12]

    Learning structured output represen- tation using deep conditional generative models,

    K. Sohn, H. Lee, and X. Yan, “Learning structured output represen- tation using deep conditional generative models,” Advances in neural information processing systems , vol. 28, 2015

    2015

  5. [13]

    Master thesis - data enrichment strategies for ai based reliability assessment of distribution grid components,

    E. P. Parra, “Master thesis - data enrichment strategies for ai based reliability assessment of distribution grid components,” 2023

    2023

  6. [14]

    Leveraging the exact likelihood of deep latent variable models,

    P.-A. Mattei and J. Frellsen, “Leveraging the exact likelihood of deep latent variable models,” 2018

    2018

  7. [15]

    Semi-supervised learning with deep generative models,

    D. P. Kingma, S. Mohamed, D. Jimenez Rezende, and M. Welling, “Semi-supervised learning with deep generative models,” Advances in neural information processing systems , vol. 27, 2014

    2014

  8. [16]

    Table for estimating the goodness of fit of empirical distributions,

    N. Smirnov, “Table for estimating the goodness of fit of empirical distributions,” The Annals of Mathematical Statistics , vol. 19, no. 2, pp. 279–281, 1948. [Online]. Available: http://www.jstor.org/stable/2236278

    1948

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