Evidence map›Paper›PMID 42701119›Full record

ArticleScientific data2026

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

Manuel Treutlein, Pascal Bothe, Marc Schmidt, Roman Hahn, Oliver Neumann, Frederik Gielnik, Ralf Mikut, Veit Hagenmeyer

Abstract read
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Manuel TreutleinInstitute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. m.treutlein@netze-bw.de.ORCID http://orcid.org/0009-0006-1071-341X
Pascal BotheAI & Analytics, Netze BW GmbH, Stuttgart, Germany.ORCID http://orcid.org/0000-0003-0262-1102
Marc SchmidtAI & Analytics, Netze BW GmbH, Stuttgart, Germany.ORCID http://orcid.org/0000-0002-9127-1708
Roman HahnAI & Analytics, Netze BW GmbH, Stuttgart, Germany.ORCID http://orcid.org/0009-0002-2115-3711
Oliver NeumannAI & Analytics, Netze BW GmbH, Stuttgart, Germany.ORCID http://orcid.org/0000-0003-4438-300X
Frederik GielnikInstitute for Electric Energy Systems and High-Voltage Technology (IEH), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.ORCID http://orcid.org/0009-0001-4628-5895
Ralf MikutInstitute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.ORCID http://orcid.org/0000-0001-9100-5496
Veit HagenmeyerInstitute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.ORCID http://orcid.org/0000-0002-3572-9083

Funding

Helmholtz Association Energy System Design (ESD)
6 · The paper itself

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.

Identifiers

PMID42701119
PMCPMC13546295

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.