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Household electricity demand is hard to predict because people's habits vary wildly. This paper embeds inferred behavioral patterns into a neural process model to forecast short-term load. Most models fail to capture the diversity of household routines, but this one does better. If your smart grid relies on a model that can't handle real people, you're in trouble.
“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. This work investigates…”
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ENTITY
Behaviour-Conditioned Neural Processes, Residential short-term load forecasting
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Automated · no editorial override
LAST OBSERVED
Aug 7, 2026