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

2026-07-17 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new Behaviour-Conditioned Neural Process framework addresses challenges in residential short-term load forecasting (STLF) by embedding inferred behavioural structure directly into a probabilistic model. This Attentive Neural Process treats each load profile as a forecasting task, utilizing a discrete latent variable for behaviour-conditioned decoder conditioning and a continuous latent variable for shared functional uncertainty. Training uses clustering-derived weak supervision, while test-time conditioning relies on context-inferred class distributions. Experiments on the Smart Grid, Smart City (SGSC) dataset show significant improvements, achieving average reductions of 7.9% in MAE and 6.9% in CRPS over a label-agnostic ANP baseline, and lower RMSE compared to fixed-window deterministic STLF baselines, especially under limited context.

Key takeaway

For Machine Learning Engineers developing residential energy forecasting solutions, this work demonstrates a robust approach to handle diverse household consumption patterns. By integrating behaviour-conditioned Neural Processes, you can achieve more accurate and uncertainty-aware short-term load forecasts, particularly in scenarios with limited context. Consider adopting this framework to improve model adaptability and reduce prediction deviations across heterogeneous user behaviours.

Key insights

Embedding inferred behavioural structure within a Neural Process enhances adaptive residential short-term load forecasting.

Principles

• Household demand is heterogeneous and temporally variable.
• Behavioural structure can be a direct forecasting mechanism.
• Single-model forecasting can handle diverse contexts.

Method

The framework uses a behaviour-conditioned Attentive Neural Process with discrete and continuous latent variables. Weak supervision from clustering guides training, while context-inferred class distributions enable test-time conditioning.

In practice

• Use clustering for weak supervision in model training.
• Condition test-time forecasts on context-inferred class distributions.

Topics

• Neural Processes
• Short-Term Load Forecasting
• Residential Energy Demand
• Behavioural Modelling
• Probabilistic Forecasting
• Smart Grid

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.