arXiv Machine Learning By Ramin Soleimani, Andrea Visentin, Dirk Pesch

Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting

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arXiv:2607. 16168v1 Announce Type: new Abstract: Residential short-term load forecasting (STLF) is challenging because household demand is heterogeneous, temporally variable, and shaped by diverse behavioural routines.

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arXiv Machine Learning
Sep 11

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

LoaDiff is a diffusion-based generative model that produces year-long, sub-hourly smart‑meter electricity consumption time series. It can be conditioned on static household attributes like appliance ownership and dynamic factors such as calendar dates and outdoor temperature. Evaluations on three residential datasets show that LoaDiff generates realistic, diverse load profiles, limits memorization, retains useful information for downstream tasks, and responds coherently to conditioning changes.

By Mariia Baranova, Adrien Petralia, Etienne Le Naour, Nathan Etourneau, Guillaume Hofmann, Themis Palpanas