这篇论文用行为条件化神经过程做家庭用电预测，比基线方法MAE降了7.9%，特别适合处理不同家庭的用电习惯差异。
该论文提出行为条件化注意力神经过程框架，将推断出的行为结构嵌入预测机制，而非仅作为外部分组信号。在SGSC数据集上，使用用户不相交的训练/验证/测试集和可变上下文长度，与标签无关的ANP基线相比，最佳变体平均降低MAE 7.9%和CRPS 6.9%。与固定窗口基线相比，该变体在所有评估预测范围上实现更低RMSE。结果表明该框架能实现跨异质家庭、上下文和预测范围的单模型不确定性感知预测。
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 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.