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LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics

arXiv 2609.11639 ↗First posted 10 Sep 2026cs.LG cs.AI eess.SPcited by 0

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Abstract (arXiv)

The energy transition is reshaping residential electricity consumption through the increasing adoption of distributed generation, electrified appliances, and demand-response programs. Understanding these evolving behaviors requires access to granular smart-meter data for applications such as load forecasting, appliance detection, and demand-side flexibility analysis. However, such data are subject to strict access restrictions and data-protection regulations. Thus, realistic synthetic alternatives are necessary. In this paper, we introduce LoaDiff, a diffusion-based generative model for year-long, sub-hourly smart-meter load curves. LoaDiff supports flexible conditioning on static household attributes, such as appliance ownership, and dynamic contextual variables, including calendar information and outdoor temperature. We evaluate the model against multiple generative baselines on three residential electricity-consumption datasets. Our experiments assess four complementary dimensions: fidelity and diversity, training-record memorization risk, downstream utility for load forecasting and appliance detection, and conditional controllability under alternative temperature conditions. The results show that LoaDiff generates realistic and diverse load profiles, achieves a favorable trade-off between generation quality and limited evidence of memorization, preserves information useful for downstream energy applications, and responds coherently to changes in conditioning variables.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 10 Sep 2026.

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19 of 39 references.

References · 19

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2022Scalable Diffusion Models with Transformerscited by 6,054
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2019ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelscited by 339
  5. 2024Diffusion-TS: Interpretable Diffusion for General Time Series Generationcited by 184
  6. 2021TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generationcited by 142
  7. 2019Quant GANs: Deep Generation of Financial Time Seriescited by 108
  8. 2021Sig-Wasserstein GANs for Time Series Generationcited by 60
  9. 2021PSA-GAN: Progressive Self Attention GANs for Synthetic Time Seriescited by 58
  10. 2023Vector Quantized Time Series Generation with a Bidirectional Prior Modelcited by 50
  11. 2024Time Weaver: A Conditional Time Series Generation Modelcited by 37
  12. 2022High-resolution synthetic residential energy use profiles for the United Statescited by 10
7 more
  1. 2023Appliance Detection Using Very Low-Frequency Smart Meter Time Seriescited by 7
  2. 2024EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion Modelscited by 7
  3. 2023ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Seriescited by 4
  4. 2025NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationaritycited by 3
  5. 2025A synthetic dataset of French electric load curves with temperature conditioningcited by 2
  6. 2025Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Seriescited by 2
  7. 2025DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electricity Consumption Time Seriescited by 1

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  1. 2025Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios
  2. 2026WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs
  3. 2023Customized Load Profiles Synthesis for Electricity Customers Based on Conditional Diffusion Models
  4. 2025External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting
  5. 2025Knowledge Distillation from Large Language Models for Household Energy Modeling
  6. 2026SynEnergy: Anomaly Semantic-Guided Diffusion for Synthetic Energy Data Generation
  7. 2025TriForecaster: A Mixture of Experts Framework for Multi-Region Electric Load Forecasting with Tri-dimensional Specialization
  8. 2026SmartMeterFM: Unifying Smart Meter Data Generative Tasks Using Flow Matching Models