mlsanityData to 14 Oct 2026

A synthetic dataset of French electric load curves with temperature conditioning

arXiv 2504.14046 ↗First posted 18 Apr 2025cs.LG cs.AIcited by 2

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Abstract (arXiv)

The undergoing energy transition is causing behavioral changes in electricity use, e.g. with self-consumption of local generation, or flexibility services for demand control. To better understand these changes and the challenges they induce, accessing individual smart meter data is crucial. Yet this is personal data under the European GDPR. A widespread use of such data requires thus to create synthetic realistic and privacy-preserving samples. This paper introduces a new synthetic load curve dataset generated by conditional latent diffusion. We also provide the contracted power, time-of-use plan and local temperature used for generation. Fidelity, utility and privacy of the dataset are thoroughly evaluated, demonstrating its good quality and thereby supporting its interest for energy modeling applications.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 18 Apr 2025.

Builds on

18 of 28 references.

References · 18

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
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  12. 2021Sig-Wasserstein GANs for Time Series Generationcited by 60
6 more
  1. 2021PSA-GAN: Progressive Self Attention GANs for Synthetic Time Seriescited by 58
  2. 2023BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecastingcited by 26
  3. 2015A Test of Relative Similarity For Model Selection in Generative Modelscited by 25
  4. 2022High-resolution synthetic residential energy use profiles for the United Statescited by 10
  5. 2024Faraday: Synthetic Smart Meter Generator for the smart gridcited by 6
  6. 2024Defining 'Good': Evaluation Framework for Synthetic Smart Meter Datacited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Are Time-Indexed Foundation Models the Future of Time Series Imputation?cited by 1
  2. 2026LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analyticscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Customized Load Profiles Synthesis for Electricity Customers Based on Conditional Diffusion Models
  2. 2025Evaluating Privacy-Utility Tradeoffs in Synthetic Smart Grid Data
  3. 2025Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios
  4. 2025Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models
  5. 2026SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation
  6. 2024Generating Synthetic Net Load Data with Physics-informed Diffusion Model
  7. 2024EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion Models
  8. 2025Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets