SynPop-DE: Synthetic population of 40 million German households using generative neural networks
SynPop-DE: 生成ニューラルネットワークを用いたドイツの4000万世帯の合成人口 (AI 翻訳)
Napiontek, Jakob, Pichler, Peter-Paul
🤖 gxceed AI 要約
日本語
本論文は、ドイツ全土の4000万世帯を対象とした合成人口データセットSynPop-DEを紹介する。家計調査の属性分布を機械学習で学習し、センサスデータで調整することで、エネルギー移行の世帯レベル影響分析などに利用可能な公開データを提供する。
English
This paper presents SynPop-DE, a synthetic population dataset of 40 million German households with 34 attributes, generated using a two-stage machine learning architecture (autoencoder + GAN) and calibrated to 2022 census data. It supports energy transition impact modeling at household level and is openly available.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
ドイツのデータだが、合成人口生成手法は日本のエネルギー移行モデリングにも応用可能。日本の家計調査データと国勢統計を組み合わせた類似データ構築の参考になる。
In the global GX context
This open synthetic population dataset enables detailed household-level energy transition modeling, filling a critical data gap. The methodology can be replicated for other countries, advancing global energy demand research.
👥 読者別の含意
🔬研究者:Use SynPop-DE for energy transition modeling or adapt the generative method to other countries.
🏢実務担当者:Datasets like SynPop-DE can inform customer segmentation or demand-side management strategies for energy utilities.
🏛政策担当者:Supports evidence-based energy policy by enabling simulation of household-level impacts of transition measures.
📄 Abstract（原文）
This is the data repository for the SynPop-DE ( Syn thetic Pop ulation of Germany) accompanying the publication: "SynPop-DE: Synthetic population of 40 million German households using generative neural networks". Introduction: Household microdata combining socio-demographic, housing, income and expenditure attributes are a core resource for many studies in quantitative social science, such as modelling the household-level impacts of the energy transition. Yet no such data are openly available for Germany’s full population. SynPop-DE provides a synthetic population of 40,235,916 households and their 81,629,116 members in all 400 German districts, calibrated to the 2022 census, with 34 attributes per household. Synthetic households are generated by estimating the joint attribute distribution of the German Household Budget Survey through a two-stage machine learning architecture. While an autoencoder first compresses high-dimensional categorical data into a continuous latent space, a generative adversarial network subsequently learns to sample new records from this representation. These records are then aligned with census marginals for all German districts using iterative proportional updating to ensure spatial representativeness. Validation along three dimensions confirms that the model learns attribute relationships and generates synthetic households that reproduce the statistical properties of the survey data (fidelity), supports downstream analyses with accuracy comparable to the original survey (utility), and prevents disclosure of individual respondents (privacy). The dataset is openly available at https://synpop.de . Data: The current data for SynPop-DE can always be found at synpop.de Code: The code to reproduce the data can be found here: https://gitlab.pik-potsdam.de/metab/synpopde
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20439916first seen 2026-06-02 04:12:08 · last seen 2026-06-08 04:13:30
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