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

Abstract: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.

10 pages, 5 figures. This paper appeared in IEEE ICDM 2026

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)

arXiv:2609.11639 [cs.LG]

(or arXiv:2609.11639v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.11639 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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