Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation

Federica Bragone; Francis Tembo; Kateryna Morozovska; Matthieu Barreau; Tor Laneryd

REVIEW 4 major objections 5 minor 26 references

Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read On the reported dataset, an 8-layer ANN predicts transformer top-oil temperature with a mean absolute error of 1.49°C, against 5.51°C for the IEC 60076-7 standard.

desk verdict A modest but honest benchmark of ANN/TiDE/TCN against IEC 60076-7 that needs a held-out test split before the headline MAE margins are taken at face value. read the letter →

arxiv 2501.16831 v1 pith:2PP557LR submitted 2025-01-28 cs.LG

Francis Tembo , Federica Bragone , Tor Laneryd , Matthieu Barreau , Kateryna Morozovska This is my paper · ORCID

classification cs.LG

keywords powertransformerstop-oiltemperaturetime-seriesforecastingartificialneuralnetworksTiDEtemporalconvolutionalquantileregressionpredictionintervals

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 on a single operating power transformer, three data-driven time-series models—a plain artificial neural network, a Time-series Dense Encoder (TiDE) built from dense layers, and a Temporal Convolutional Network (TCN) using dilated causal convolutions—estimate top-oil temperature more accurately than the IEC 60076-7 standard thermal model. The best model, an ANN with 8 layers and 128 neurons per layer using a 4-hour look-back, achieves a mean absolute error of 1.49 °C on the validation period, against 5.51 °C for the IEC model, with the ANN's mean squared error roughly 16 times smaller. The paper extends the same models to estimate temperature rise over ambient and uses quantile regression to build conditional prediction intervals, reporting that the best interval-covering model (TiDE) covers 73% of validation samples within a nominal 98% interval. If these numbers hold, transformer operators could monitor thermal stress more accurately without relying on transformer-specific thermal parameters.

What carries the argument

The comparison is carried by three neural architectures—a feedforward ANN, TiDE's dense encoder–decoder with residual connections and temporal decoding, and a TCN with dilated causal convolutions—run as one-step autoregressive predictors and scored by mean absolute error and mean squared error against the discretized IEC 60076-7 differential equation for top-oil temperature. Quantile regression replaces squared-error loss with an asymmetric absolute loss indexed by α, and pairs of estimated quantiles form the nominal 98% prediction interval PI98. The look-back window, layer or filter counts, optimizer, and learning rate are the handles adjusted by the grid search.

What would settle it

Freeze all hyperparameters and architecture choices using only the training period, then run the selected ANN and the IEC 60076-7 model on a new continuous measurement period (for example, summer months or a different transformer) and compare their MAE; if the ANN no longer beats the standard model by a substantial margin, the claimed superiority is shown to come from tuning on the validation set rather than from the model family itself.

Watch

Extended reading notes

Core claim

The central claim, stated on the paper's own terms, is that all three proposed neural models outperform the IEC 60076-7 loading-guide model for autoregressive top-oil temperature estimation: the ANN reaches MAE 1.49°C and MSE 3.49°C, TiDE reaches 1.86°C and 6.02, the TCN reaches 3.49°C and 18.18, while the IEC model records 5.51°C and 57.25. The same ranking holds for temperature rise over ambient, where the ANN again leads with MAE 1.53°C. Under quantile regression, the models produce nominal 98% intervals with observed coverage between 0.16 (ANN) and 0.77 (TCN), and the paper concludes that the TiDE model offers a useful compromise of error and coverage. The paper leaves as future work the combination of quantile regression with conformal calibration to achieve coverage guarantees.

Load-bearing premise

The whole comparison rests on treating the 42-day validation period as a fair test, yet the same period was used to decide which model configurations to report, so the headline error gap may be partly a selection artifact.

Editorial extensions

If this is right

If the reported errors replicate on other units, operators could make dynamic-loading decisions with a top-oil estimate that is roughly 3.7 times closer to measurements than the standard model's.
The data-driven models require no transformer-specific thermal constants, so they can be applied where nameplate parameters are missing or the cooling mode is unknown.
Quantile regression provides a prediction interval around each estimate, although the best observed coverage (73–77%) is below the nominal 98% target.
The same autoregressive pipeline estimates temperature rise over ambient, the quantity most directly tied to insulation ageing.
The best-performing configuration is a plain ANN, so the accuracy gain does not depend on the most complex architecture.

Reading between the lines

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

The size of the reported gap (1.49 vs 5.51°C) should be re-estimated on an independent test period, because the same validation data were used to select the best configurations.
The data cover one transformer over roughly four and a half months, so how the ranking holds across transformers, forced-air cooling, and seasonal extremes remains untested.
The under-coverage of the nominal 98% intervals suggests that quantile regression alone is not enough; conformalized quantile regression, which the paper mentions as future work, directly targets the coverage property.
Because evaluation is autoregressive, errors compound over time; direct multi-step forecasts compared against the IEC iterative solution would reveal whether the advantage persists at longer horizons.

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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 read of the transformer top-oil paper. What's genuinely new is modest but real: TiDE and TCN haven't been benchmarked against IEC 60076-7 on real transformer data before, and the quantile-regression intervals are a sensible addition. The paper is also honest in a way that counts — Table 6 plainly shows the 98% intervals only cover 16–77% of validation points, and the text admits the intervals fail (9). That's the kind of reporting we want more of.

The central comparison is plausible. On this one transformer, the ANN at 1.49 °C MAE beats the IEC model at 5.51 °C by a wide margin, and the figure shows the ML models track the measurements much better. That is consistent with prior work [12] on nine transformers, so it's not a surprising result, but it's confirmation on a new dataset with newer architectures.

The soft spots are real and they mostly reduce to one issue: the validation set did double duty. Section 4 describes a grid search over 27 ANN, 18 TCN, and 6 TiDE configurations, then Tables 4 and 6 report the 'best configurations' on that same validation period. So the headline MAE/MSE numbers are minima over a searched set, not unbiased out-of-sample estimates. The margin over IEC is large enough that I doubt it would vanish on a true test set, but we can't know from this paper. A separate test period is needed. Relatedly, the IEC baseline is under-specified: ONAN is assumed, but the actual parameter values and the finite-difference step Dt aren't reported, so the comparison denominator is soft.

Minor points: single transformer, single 42-day validation stretch, and the distribution shift (validation has lower temperatures than training) isn't discussed as a challenge. The abstract overstates the interval result — 'sufficient coverage' is not what Table 6 shows.

Bottom line: this is a legitimate but incremental benchmark. It deserves a serious referee, with the expectation that the authors add a held-out test split, report full baseline parameters, and recalibrate or reframe the interval claim. I'd not cite it myself, but I'd send it back for revision rather than desk-reject.

Referee Report

4 major / 5 minor

Summary. The paper compares three data-driven time-series models—an ANN, a TCN, and a TiDE model implemented with the Darts library—against the IEC 60076-7 thermal model for estimating top-oil temperature of a single operating power transformer. The data are five-minute measurements over 190 days, split into a training period and a validation period, and all neural models are evaluated autoregressively over the validation set. The headline results in Table 4 show the ANN reaching a top-oil MAE of 1.49 °C and MSE of 3.49, versus 5.51 and 57.25 for the IEC model, and the text concludes that all proposed models outperform the standard model. The paper also applies quantile regression to build 98% prediction intervals, reporting coverage probabilities in Table 6. The authors acknowledge that the intervals do not satisfy the nominal coverage condition (9) and suggest conformal methods as future work.

Significance. If the reported comparison were unbiased, the paper would provide useful evidence that simple neural models can beat the standard IEC thermal model on this transformer, and the quantile-regression extension would be a practical contribution for uncertainty-aware monitoring. The study uses real operational data, an external standard as baseline, and a temporal train/validation split, which are all strengths. However, the central outperformance claim is weakened by the fact that hyperparameters are selected on the same validation set used to report final errors, and the IEC baseline is incompletely specified. In addition, the abstract's statement that the best model provides 'sufficient coverage' is contradicted by the paper's own Table 6 and Section 4.2, where the best coverage is 0.77 against a nominal 0.98. These issues are fixable with a held-out test period, precise baseline parameter reporting, and recalibrated or conformalized intervals, but they currently prevent the results from being taken at face value.

major comments (4)

[Section 4, Tables 4 and 6] The central comparison is underminded by using the validation set both for model selection and for reporting final performance. Section 4 states that the authors employed 'a mixed approach of trial and error alongside a systematic grid search' over the hyperparameters in Table 2, and Tables 4 and 6 then report the best configurations on the same validation period. Because the best MAE/MSE is a minimum over the searched configurations (27 ANN, 18 TCN, and 6 TiDE candidates), the reported values are optimistically biased estimates of out-of-sample error, and the margin over the IEC baseline (ANN MAE 1.49 °C vs 5.51 °C) is partly a selection artifact. A held-out test period, or nested cross-validation with selection performed only on training folds, is needed before the outperformance claim is established.
[Section 3 and Section 4.1, Eq. (15)] The IEC 60076-7 baseline is not sufficiently specified for the comparison to be reproducible or interpretable. The model parameters Ψ, ΔTor, χ, k11, and τo are not reported; the cooling mode is assumed to be ONAN without supporting information; and the finite-difference time step Dt is described only as chosen from 'numerous values' within a sensible range. Without the parameter values, the fitted or assumed baseline, and a sensitivity analysis to these choices, the reader cannot assess whether the large IEC errors in Table 4 reflect the standard model's inherent accuracy or an unfavorable or arbitrary parameterization.
[Abstract, Section 4.2, Table 6] The abstract claims that the best-performing model 'successfully estimates conditional quantiles that provide sufficient coverage,' but this is inconsistent with the reported results. Table 6 shows mean coverage of 0.16 for the ANN, 0.77 for the TCN, and 0.73 for the TiDE model for a nominal 98% prediction interval, and Section 4.2 explicitly states that the estimated conditional prediction interval fails to satisfy Eq. (9). The paper should either define a quantitative coverage target appropriate for the application, revise the abstract and conclusions to match the actual coverage values, or add a calibration step such as conformalized quantile regression.
[Section 4.1, Table 4] No measure of uncertainty is attached to the point-estimate comparisons. The models are trained once per configuration, and differences such as ANN MAE 1.49 vs TiDE MAE 1.86 vs TCN MAE 3.49 are reported without repeated-seed variability, confidence intervals, or a statistical test. Given that the margin over the IEC baseline is the paper's main quantitative claim, the reader needs at least seed-based error bars or a paired significance test to know whether the ranking of the proposed models is stable and whether the comparison to IEC is robust.

minor comments (5)

[Section 3] The sentence 'the validation set has samples of lower top-oil temperatures that are not present in the validation set' is self-contradictory; the final phrase should presumably refer to the training set, and Figure 3 appears to illustrate this distribution shift.
[Table 4 caption] The word 'Perfomance' is misspelled and should be 'Performance.'
[Section 4.1] The sentence 'the systematic grid search for the ANN compared to the more complex architectures because they take less time to train' is grammatically incomplete; it should say something like 'the systematic grid search for the ANN was more extensive than for the more complex architectures because ANNs take less time to train.'
[Section 4.2] The phrase 'the temperature measurements fairly fall into the prediction interval' is informal; 'largely fall within' would be clearer, especially since the measured coverage is only 0.73.
[Section 5] The discussion would benefit from a short statement on data and code availability, since the comparison depends on specific preprocessing choices (interpolation of missing values, matching of ambient data) that are described but cannot currently be reproduced without the dataset.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: ML models are trained on a separate time period and compared against an external IEC baseline; the only caveats are non-load-bearing self-citations and validation-set use for hyperparameter selection, which is a soundness issue rather than circularity.

full rationale

The paper's derivation chain is not circular. The deep-learning models (ANN, TCN, TiDE) are trained on the training split (2020-07-02 to 2020-11-19) and evaluated on a later validation split (2020-11-19 to 2021-01-01); the labels on the validation set are not used to define the model outputs. The IEC 60076-7 baseline is an external standard, solved via finite differences with assumed ONAN-cooling constants, and its parameters are not fitted to the validation data. The self-citations [13-16] are prior PINN work cited as context for future comparisons; they do not supply the fitted configurations or the validation metrics used here, so they are not load-bearing. The only substantive concern is experimental design: Section 4 states that 'we employ a mixed approach of trial and error alongside a systematic grid search to determine the optimal configuration for each model,' and Tables 4 and 6 then report the 'best configurations' on the same validation period. This means the reported MAE/MSE values are minima over the searched hyperparameter grid and may be optimistically biased; however, this is a statistical selection artifact, not a definitional reduction of the prediction to its inputs. The model errors are not equal to the validation labels by construction, and the comparison to IEC does not depend on any fitted parameter renamed as a prediction. Accordingly, no circular step is exhibited, and the paper is self-contained against an external benchmark. Score 2 reflects the presence of minor, non-load-bearing self-citations and the validation-selection caveat, not actual circularity.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central comparison rests on standard thermal-model assumptions (ONAN mode, IEC equation, weather-station ambient) and on model hyperparameters chosen by grid search. No new physical entities are introduced. The most fragile entries are the ONAN assumption and the use of the validation set for model selection.

free parameters (5)

ANN architecture (layers, neurons per layer, look-back) = 8 layers, 128 neurons, 4-hour look-back (Table 4)
Chosen by grid search; this configuration produced the reported 1.49 C MAE.
TCN architecture (kernel size, number of filters, look-back) = kernel 2, 16 filters, 4-hour look-back (Table 4)
Chosen by grid search; this configuration produced 3.49 C MAE.
TiDE architecture (decoder output dimension, temporal decoder hidden width, look-back) = decoder output 8, temporal decoder hidden 8, 4-hour look-back (Table 4)
Chosen by grid search; this configuration produced 1.86 C MAE.
Optimizer training settings (learning rate, batch size, max epochs) = ANN: lr 1e-5, batch 256, 4000 epochs; TCN: lr 1e-4, batch 512, 500; TiDE: lr 1e-6, batch 512, 100 (Tables 3 and 5)
Initial experiments tuned these settings before the architecture search; they materially affect reported performance.
IEC finite-difference time step Dt = not reported
Section 4: 'numerous values of Dt were considered within a sensible range' but the chosen value is not stated, affecting the IEC baseline error.

assumptions (5)

domain assumption IEC 60076-7 differential equation (Eq. 15) is a valid thermal model for the operating transformer and is an appropriate baseline.
Used as the baseline throughout Section 4; no parameters are fitted or validated against this transformer's nameplate/cooling data.
domain assumption The transformer operates exclusively in ONAN mode (natural oil/air convection, no forced cooling).
Section 4, paragraph 1: cooling-stage data was unavailable, so forced-air time constants are ignored; if false, the IEC baseline is mis-specified.
domain assumption Ambient temperature from the nearest SMHI weather station, linearly interpolated to 5-minute resolution, approximates the local ambient at the transformer site.
Section 3: hourly station data are interpolated; distance to the station and interpolation error are not assessed.
domain assumption Replacing missing top-oil readings with the average of adjacent values does not bias the comparison.
Section 3, data cleaning step; no sensitivity analysis to the imputation rule.
ad hoc to paper The validation period is an honest out-of-sample test of model performance.
This is the premise of the reported MAE/MSE. It is partly violated because the same validation set appears to have been used for hyperparameter selection (Section 4, Tables 4 and 6).

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Cite this review

Pith. "Pith review of Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation." pith.science (2026). https://pith.science/paper/2PP557LR

@misc{pith2026250116831,
  author       = {Pith},
  title        = {Pith review of: Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2PP557LR}},
  note         = {Machine review of arXiv:2501.16831}
}

read the original abstract

Power transformers are subjected to electrical currents and temperature fluctuations that, if not properly controlled, can lead to major deterioration of their insulation system. Therefore, monitoring the temperature of a power transformer is fundamental to ensure a long-term operational life. Models presented in the IEC 60076-7 and IEEE standards, for example, monitor the temperature by calculating the top-oil and the hot-spot temperatures. However, these models are not very accurate and rely on the power transformers' properties. This paper focuses on finding an alternative method to predict the top-oil temperatures given previous measurements. Given the large quantities of data available, machine learning methods for time series forecasting are analyzed and compared to the real measurements and the corresponding prediction of the IEC standard. The methods tested are Artificial Neural Networks (ANNs), Time-series Dense Encoder (TiDE), and Temporal Convolutional Networks (TCN) using different combinations of historical measurements. Each of these methods outperformed the IEC 60076-7 model and they are extended to estimate the temperature rise over ambient. To enhance prediction reliability, we explore the application of quantile regression to construct prediction intervals for the expected top-oil temperature ranges. The best-performing model successfully estimates conditional quantiles that provide sufficient coverage.

Figures

Figures reproduced from arXiv: 2501.16831 by the authors.

**Figure 2.** Overview of TiDE architecture. 7 [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Top-oil temperature measurements over the training and validation datasets. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Top-oil temperature estimates from the proposed models against measurements. [PITH_FULL_IMAGE:figures/full_fig_p017_4.png]

**Figure 5.** Figure 5: Top-oil temperature estimates for each model at the conditional quantile [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]

**Figure 6.** Figure 6: Top-oil temperature measurements against the estimated conditional quantile [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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Doolgindachbaporn, G

A. Doolgindachbaporn, G. Callender, P. L. Lewin, E. Simonson, G. Wil- son, Data driven transformer thermal model for condition monitor- ing, IEEE Transactions on Power Delivery 37 (4) (2022) 3133–3141. doi:10.1109/TPWRD.2021.3123957

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Michiorri, H.-M

A. Michiorri, H.-M. Nguyen, S. Alessandrini, J. B. Bremnes, S. Dierer, E. Ferrero, B.-E. Nygaard, P. Pinson, N. Thomaidis, S. Uski, Forecasting for dynamic line rating, Renewable and Sustainable Energy Reviews 52 (2015) 1713–1730. doi:https: //doi.org/10.1016/j.rser.2015.07.134. URL https://www.sciencedirect.com/science/article/pii/ S1364032115007819

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Estanqueiro, C

A. Estanqueiro, C. Ahlrot, J. Duque, D. Santos, J. P. Gentle, A. W. Abboud, K. Morozovska, P. Hilber, L. S¨ oder, T. Kanefendt, Dlr use for optimization of network design withvery large wind (and vre) penetra- tion, in: 17th Wind Integration Workshop, 2018

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work page doi:10.1016/j.epsr.2019.02.026 2019