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Life sciences · Preprint
arXiv · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
LoaDiff is a diffusion-based generative model designed to create realistic synthetic smart-meter electricity consumption time series conditioned on household attributes and weather. The work is a proof-of-concept technical study showing the model produces diverse profiles with low memorization risk and utility for downstream forecasting and appliance detection tasks, but lacks independent validation and real-world deployment evidence.
Technical evaluation of a generative model against baselines on three datasets. Residential electricity consumers from three smart-meter datasets; specific inclusion/exclusion criteria and sample characteristics not stated in abstract.. Intervention: LoaDiff: diffusion-based generative model for year-long, sub-hourly smart-meter load curves, conditioned on static household attributes (e.g. appliance ownership) and dynamic variables (calendar, outdoor temperature). Compared with: Multiple generative baselines (not named or described in abstract).
LoaDiff generates realistic and diverse load profiles across three residential datasets Model achieves favorable trade-off between generation quality and limited memorization risk Synthetic data preserves information useful for downstream energy applications (load forecasting, appliance detection)
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A novel machine-learning tool for synthetic data generation, evaluated on technical performance metrics (fidelity, memorization, downstream utility) but without validation in a real-world clinical or operational energy-management setting.
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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.
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