发表机构
School of Computer Science and Information Technology, University College Cork（科克大学计算机科学与信息技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究住宅短期负荷预测难题，提出行为条件注意力神经过程框架，将行为结构嵌入预测机制，通过聚类信息弱监督训练，实验证明该框架在多方面优于基线，支持跨多种情况的单模型、不确定性感知预测。
AI 中文摘要
住宅短期负荷预测具有挑战性，因为家庭需求具有异质性、随时间变化且受多种行为习惯影响。本文研究能否将推断出的行为结构嵌入基于神经过程的概率模型预测机制中，而非仅用作外部分组信号，以进行上下文条件住宅短期负荷预测。提出行为条件注意力神经过程框架，将每个负荷曲线视为预测任务。行为结构由从可用上下文中推断出的离散潜在变量表示，用于行为条件解码器调节，连续潜在变量捕获异构曲线间的共享功能不确定性。训练时聚类信息提供弱监督，测试时仅依赖上下文推断的类分布。实验表明该框架在多方面有改进，支持跨异构家庭、上下文和时间范围的单模型、不确定性感知预测。
英文摘要
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 latent variable inferred from the available context and used for behaviour-conditioned decoder conditioning, while a continuous latent variable captures shared functional uncertainty across heterogeneous profiles. To enable conditioning without ground-truth behavioural labels, clustering-derived information provides weak supervision during training, whereas test-time conditioning relies only on context-inferred class distributions. Experiments on the Smart Grid, Smart City (SGSC) dataset use user-disjoint train/validation/test splits, variable context lengths, and multi-step forecast horizons, with comparisons against a label-agnostic ANP baseline and fixed-window deterministic STLF baselines. The proposed variants improve MAE and CRPS over ANP across horizons and context settings, with the largest gains under limited context. The best-performing variant achieves average reductions of 7.9% in MAE and 6.9% in CRPS relative to ANP. Compared with fixed-window baselines, this variant achieves lower RMSE across all evaluated horizons while maintaining competitive MAE, suggesting fewer large prediction deviations under heterogeneous consumption patterns. These results support single-model, uncertainty-aware forecasting across heterogeneous households, contexts, and horizons.
CommentsPreprint. 45 pages, 6 figures
