Real-world energy data of 200 feeders from low-voltage grids with metadata in Germany over two years

Manuel Treutlein; Marc Schmidt; Oliver Neumann; Pascal Bothe; Ralf Mikut; Roman Hahn; Veit Hagenmeyer

Real-world energy data of 200 feeders from low-voltage grids with metadata in Germany over two years

T0 review · 0 major / 6 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read A new public dataset records 200 German low-voltage feeders at one-minute resolution over two years, with detailed metadata on solar panels, heat pumps, and EV chargers.

desk verdict A genuinely useful public dataset of 200 German LV feeders at 1-minute resolution over two years, described by a careful, transparent data descriptor; the main risk is metadata accuracy, which the authors openly acknowledge. read the letter →

arxiv 2602.03521 v2 pith:JADOWCBY submitted 2026-02-03 eess.SY cs.SY

Manuel Treutlein , Pascal Bothe , Marc Schmidt , Roman Hahn , Oliver Neumann , Ralf Mikut , Veit Hagenmeyer This is my paper · ORCID

classification eess.SYcs.SY

keywords low-voltagegridfeedermeasurementspublicdatasetphotovoltaicheatpumpsEVchargersloadforecastingweatherdata

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 presents FeederBW, a real-world dataset of 200 low-voltage feeders in Germany measured every minute for two years (April 2023–March 2025). It combines feeder-level electrical measurements—per-phase currents, active and reactive power, voltage, and power factor—with hourly weather data and event-driven feeder metadata that tracks the installed power of low-carbon technologies. The authors argue that this combination fills a scarcity of public, grid-measured low-voltage data, which is essential for reproducible research in load forecasting, non-intrusive load monitoring, synthetic data generation, and understanding how weather and decentralized generation interact. A sympathetic reader would see this as a substantial, curated contribution to open energy data, useful for both machine learning and grid operation studies.

What carries the argument

The dataset itself is the central object, produced by a measurement chain: clip-on current sensors on each feeder phase measure RMS currents, a gateway measures a single-phase voltage and derives active/reactive power and power factor, and a customer management system supplies equipment-level metadata, matched via zip code to hourly weather from a numerical weather prediction model. A set of selection filters (radial grid, ≥90% data completeness, privacy thresholds, capacity utilization) ensures high-quality, interpretable feeders.

What would settle it

Compare the recorded installed PV capacity against the measured midday feed-in peaks for a random sample of feeders; if many feeders show persistent unexplained shortfalls or surpluses, the metadata's trustworthiness—and with it the dataset's flagship interpretability—is called into question.

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Extended reading notes

Core claim

FeederBW is a dataset of 200 low-voltage feeders in Baden-Württemberg, Germany, containing 209,189,649 rows of one-minute feeder measurements plus hourly weather data and 1,608 rows of event-driven metadata. The measurements include voltage (one phase), active/reactive/apparent power, phase currents, and power factors, with about 99.36% completeness. The metadata records housing units, industry/commerce counts, installed power of PV, batteries, EV chargers, heat pumps, storage heaters, and other equipment, updated when customer connections change. The paper demonstrates plausible patterns—weekly load shapes, seasonal feed-in, and a clear two-year increase in installed PV and battery capacity

Load-bearing premise

The feeder metadata from the utility's customer management system accurately reflects the actual equipment, housing units, and industrial loads connected to each feeder, despite acknowledged unregistered devices and registration delays.

Editorial extensions

If this is right

Researchers can train and benchmark load forecasting models on real, minute-level feeder data with weather context, improving reproducibility in low-voltage research.
Feeder-level non-intrusive load monitoring becomes feasible on aggregated residential/commercial loads, potentially identifying the operation of heat pumps, EV chargers, and PV systems.
The combination of weather and metadata allows studying how solar irradiance, temperature, and installed low-carbon capacity drive feeder load and feed-in patterns.
The two-year span with recorded PV installation events provides a natural experiment for estimating the grid impact of rooftop solar and battery adoption.
The dataset can serve as a realistic basis for generating synthetic low-voltage grid scenarios, supporting power flow simulations without exposing topology.

Reading between the lines

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

The metadata accuracy is the key risk: unregistered equipment, registration-to-operation delays, and topology changes not documented in the data could systematically bias any model trained on FeederBW, especially for feeders where such events are common.
Because only one phase voltage is measured and the formula S = sqrt(P² + Q²) fails under opposing phase flows, users should treat apparent power as the safer thermal indicator, a nuance the paper explicitly notes but which might be overlooked in practice.
The recommended locational and temporal splits could become a de facto benchmark, but the paper does not validate them across many algorithms; users may need to test sensitivity to split choice.
Combining FeederBW with publicly available synthetic grid topologies (not included) could enable full power-flow studies, but such integration is left for future work.

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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

Bottom line: this is a data paper worth taking seriously. The authors release a real, substantial dataset — 200 LV feeders in Baden-Württemberg, 1-minute measurements over two years, weather data, and event-driven feeder metadata — on Zenodo with a DOI. That is a real contribution. The field has few public grid-measured feeder datasets this size, and none with this combination of temporal resolution and metadata depth. I believe the existence claim is credible and the paper honestly describes how the data was collected.

What the paper does well: the methods are concrete. They name the SMIGHT sensors, give the 3% accuracy, explain the energy-harvesting constraint (minimum 32 A), and state the PEN replacement policy. They report completeness (99.36% rows, 25.6% NULL PEN values explained by uninstrumented PEN). They discuss the single-phase voltage limitation and why S != sqrt(P^2+Q^2) occurs. The metadata limitations — unregistered equipment, assignment errors, registration vs operation dates, undocumented switching — are explicitly described. The recommended train-test splits are a sensible touch. The selection criteria are clear and the bias they introduce is acknowledged.

Now the soft spots, in proportion. The technical validation is mostly qualitative. Weekly quantile plots of active power for four feeders show plausible patterns, but there is no systematic error analysis against, say, a calibrated reference measurement. That is not a fatal flaw for a data descriptor — the reader can verify the data themselves — but it does mean the 3% accuracy claim is taken on trust. The metadata is the weakest link. The authors say it comes from the DSO's customer management system, has event-driven entries, and can contain errors. If those errors are systematic, some of the feeder-level interpretability claims weaken. The authors do not quantify metadata accuracy. A direct comparison of registered PV capacity against measured feed-in for a subset would have strengthened the paper. Also, the 'individual data check' blacklist is opaque — we don't know what was excluded and why. That is acceptable but limits reproducibility of the selection process.

I do not see a load-bearing flaw. The citation pattern is fine; self-citations to the data platform and a prior pseudo-measurement paper are relevant, not circular. The central claim — the dataset exists and is described accurately — is testable by downloading the Zenodo deposit.

Who is this for: anyone doing ML on distribution grids — forecasting, NILM at the feeder level, synthetic data generation, or studies of PV/heat pump impact. Serious referee time is warranted. I would accept this into the record, with a request for more systematic validation of the power measurements and metadata if the journal wants it.

Referee Report

0 major / 6 minor

Summary. The paper describes FeederBW, a public dataset of real-world measurements from 200 low-voltage feeders in Baden-Württemberg, Germany, over two years (April 2023–March 2025). The dataset comprises 209,189,649 rows of one-minute feeder measurements (voltage, phase currents, active/reactive/apparent power, power factors, PEN current), event-driven feeder metadata (housing units, installed capacities of PV, batteries, heat pumps, EV chargers, etc., and aggregated industry/commerce consumption), and hourly weather data from the ICON-D2 NWP model. The authors describe the measurement hardware and accuracy, selection criteria, data records, technical validation using quantile profiles, and detailed limitations. The dataset is available on Zenodo with a DOI, and the paper recommends train-test splits for ML applications.

Significance. If the dataset exists as described, FeederBW fills a clear gap in publicly available low-voltage grid data: it combines a relatively large number of feeders (200), one-minute resolution, weather data, and detailed consumer/producer metadata, which is rare in existing datasets. The paper is commendably transparent about hardware limitations (3% accuracy, single-phase voltage, PEN measurement issues), metadata error sources, and selection biases. The public Zenodo release with a DOI and the explicit train-test split recommendations are practical strengths that support reproducibility and comparability of ML studies. No code is provided, and the technical validation is qualitative, but the availability of the data itself makes this a useful community resource.

minor comments (6)

[Limitations in the feeder measurement data] The definition of λ_tot is garbled in the sentence: 'filtered based on the total power factor λ_tot = P λL1 +λL2 +λL3'. It should presumably read λ_tot = λ_L1 + λ_L2 + λ_L3. In the same paragraph, the active currents for feeder F127 are listed as I_L1,act = −43 A, I_L2,act = −48 A and I_L1,act = 84 A; the third current should be I_L3,act. Please correct these typographical errors.
[Limitations in the feeder metadata] The text contains several wording errors: 'this is not necessary the case' should be 'not necessarily the case'; 'for shops and hairdresser' should be 'shops and hairdressers'; 'F181 has only around two third' should be 'two thirds'. These are minor but should be fixed.
[Limitations in the weather data] The sentence 'Null values only occur in columns now height m' contains a typo: 'now height' should be 'snow height'. Also consider rephrasing the following sentence 'In applications and models you can assume 0 m' to a more formal style, e.g., 'A value of 0 m can be assumed'.
[Technical Validation] The validation is largely qualitative, based on four weekly quantile plots and two time-series plots. The statement 'we conducted extensive checks and validation to all of them' is not backed by concrete numbers. Consider adding a small table of summary statistics (e.g., per-feeder ranges of active power, missing-data rates) or a short quantitative description of the checks performed on current, voltage, and power-factor plausibility. This would strengthen the data quality assessment without requiring exhaustive validation.
[Methods / Data Records] The weather data are outputs of the numerical weather prediction model ICON-D2, not ground-based station measurements. This is stated in the Methods, but readers may overlook it in Table 1 and the Data Records. Please explicitly note in the weather data section or as a footnote to Table 1 that these are NWP model outputs, not direct observations.
[Further background information] There are several language issues in this section: 'a obligation' should be 'an obligation', 'prized' should be 'priced', and 'shortly summarize' should be 'briefly summarize'. Also, the sentence 'The data is collected after accounting for grid losses in the LV grid' is ambiguous; if the measurements are at the feeder, they include network losses, so consider rephrasing to clarify the relationship between measured values and customer consumption.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the paper is a data descriptor whose central claim is the existence and properties of FeederBW, not a derived prediction; self-citations are illustrative, not load-bearing.

full rationale

FeederBW is a dataset descriptor, and its central claim is empirical existence: real-world feeder measurements, metadata, and weather data are collected, filtered, and published on Zenodo. There is no derivation chain in which an output quantity is defined by, or fitted to, an input quantity. The 'Technical Validation' section compares measured active-power weekly profiles to feeder metadata (e.g., storage heaters in F160, PV feed-in in F97); this is a consistency check of real measurements against independent registry data, not a prediction obtained from a fitted parameter. The selection criteria (radial grids, time period, measurement completeness, privacy thresholds, capacity utilization) are curation filters, not circular definitions of the dataset's content. The paper cites prior work by overlapping authors ([22] on pseudo-measurements, [26] on the data platform), but these citations are used as example applications or internal infrastructure, not as evidence that defines or validates the dataset itself. The manuscript explicitly and prominently discloses limitations in feeder measurement data and feeder metadata (unregistered equipment, inaccurate assignment, registration/operation date mismatches, undocumented switching events), which are honest caveats rather than hidden circularity. The only remaining check is external: confirming that the Zenodo deposit matches the stated row counts and schemas. Absence of that verification is not a circularity defect. Therefore the appropriate score is low, with no specific circular step identified.

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

The dataset's central claim does not rest on any fitted parameters or invented entities. It relies on domain assumptions about sensor accuracy, metadata correctness, representativeness of weather data, and the validity of the selection criteria. These are clearly stated and limitations are discussed.

assumptions (5)

domain assumption The SMIGHT Grid 2 sensor measurements are accurate to the stated 3% tolerance for currents between 5 and 400 A.
The dataset's power and current values are based on these sensors; if the accuracy is worse than stated, the derived power values could be more uncertain. Section 'Feeder measurement data'.
domain assumption The voltage measured on one phase at the busbar is representative enough to compute three-phase power values.
All power values are calculated using this single-phase voltage. Phase imbalances or voltage asymmetries could introduce errors. Sections 'Feeder measurement data' and 'Limitations in the feeder measurement data'.
domain assumption The feeder metadata from the DSO's customer management system correctly describes the downstream customers and equipment.
Metadata is the dataset's unique feature; the paper acknowledges unregistered equipment and assignment inaccuracies. Section 'Limitations in the feeder metadata'.
domain assumption Weather data from the ICON-D2 model, aggregated by zip code, is representative of the local conditions at each feeder.
Weather is matched by zip code, so local microclimates could differ. Section 'Weather data'.
domain assumption The selection criteria (radial grids, ≥90% completeness, ≥5 housing units, capacity utilization threshold) yield a dataset appropriate for the claimed applications.
These filters introduce bias toward high-utilization feeders, which may not represent typical LV grids. Section 'Data selection process'.

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Pith. "Pith review of Real-world energy data of 200 feeders from low-voltage grids with metadata in Germany over two years." pith.science (2026). https://pith.science/paper/JADOWCBY

@misc{pith2026260203521,
  author       = {Pith},
  title        = {Pith review of: Real-world energy data of 200 feeders from low-voltage grids with metadata in Germany over two years},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JADOWCBY}},
  note         = {Machine review of arXiv:2602.03521}
}

read the original abstract

The last mile of the distribution grid is crucial for a successful energy transition, as more low-carbon technology like photovoltaic systems, heat pumps, and electric vehicle chargers connect to the low-voltage grid. Despite considerable challenges in operation and planning, researchers often lack access to suitable low-voltage grid data. To address this, we present the FeederBW dataset with data recorded by the German distribution system operator Netze BW. It offers real-world energy data from 200 low-voltage feeders over two years (2023-2025) with weather information and detailed metadata, including changes in low-carbon technology installations. The dataset includes feeder-specific details such as the number of housing units, installed power of low-carbon technology, and aggregated industrial energy data. Furthermore, high photovoltaic feed-in and one-minute temporal resolution makes the dataset unique. FeederBW supports various applications, including machine learning for load forecasting, conducting non-intrusive load monitoring, generating synthetic data, and analyzing the interplay between weather, feeder measurements, and metadata. The dataset reveals insightful patterns and clearly reflects the growing impact of low-carbon technology on low-voltage grids.

Figures

Figures reproduced from arXiv: 2602.03521 by the authors.

**Figure 2.** Example for sensors and gateway used in the MV/LV substations to measure the LV feeders. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Selection criteria for the 200 LV feeders published in the FeederBW dataset. All the published [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Active power weekly quantile profiles from four LV feeders showing diverse characteristics. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

**Figure 5.** Figure 5: Active power plotted over two years for selected LV feeders. A seasonal effect in winter and [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]

**Figure 6.** Figure 6: Average installed power per LV feeder of all consumers and producers in the feeder metadata [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods
cs.LG 2026-07 conditional novelty 6.0 of 10

Transformers—especially a standard encoder-decoder—yield the lowest hourly load forecast errors across TSO, low-voltage feeder, and client-level datasets, with 6.6–10.7% error reduction over the best non-Transformer baseline.
FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting
cs.LG 2026-04 unverdicted novelty 6.0 of 10

Foundation models outperform dataset-specific machine learning in energy time series forecasting across 54 datasets in 9 categories.

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Reviewed August 3, 2026 · model on record in the stance chip above.

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