Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting
Residential short-term load forecasting (STLF) is challenging because household demand is heterogeneous, temporally variable, and shaped by diverse behavioural routines. This work investigates whether inferred behavioural structure can be embedded within the forecasting mechanism of a Neural Process-based probabilistic model, rather than used only as an external grouping signal, for context-conditioned residential STLF. We propose a behaviour-conditioned Attentive Neural Process framework that treats each load profile as a forecasting task. Behavioural structure is represented by a discrete la
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- FuzzySimilar title/name (fuzzy) · 59%amazon-science/chronos-forecasting →
“Fuzzy title match (0.73): “Behaviour-Conditioned Neural Processes for Adaptive Resident” ≈ “amazon-science/chronos-forecasting””
- FuzzySimilar title/name (fuzzy) · 59%sktime/pytorch-forecasting →
“Fuzzy title match (0.73): “Behaviour-Conditioned Neural Processes for Adaptive Resident” ≈ “sktime/pytorch-forecasting””
- LinkedLinked via arxiv author · 85%Ramin Soleimani →
“Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting”
- LinkedLinked via arxiv author · 85%Andrea Visentin →
“Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting”
- LinkedLinked via arxiv author · 85%Dirk Pesch →
“Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting”