发表机构
Xinjiang University（新疆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对NILM跨家庭泛化问题，提出标签保持聚合重组与预测一致性训练策略，在多电器架构上显著降低REDD、UK-DALE和REFIT的平均绝对误差，且无推理开销。
AI 中文摘要
非侵入式负荷监测（NILM）从聚合功率中估计电器功率序列，但基于源家庭训练的模型在未见过的家庭中通常精度下降。聚合功率还包含来自其他电器的负载和测量误差，因此预测可能依赖于与源家庭目标共现的残余背景。时间对齐的子表计测量和聚合功率的加性分解揭示了一种窗口式监督未使用的关系：聚合窗口可以通过仅替换其残余背景而保留所有建模目标电器功率序列的逐点值来重组。我们将标签保持的聚合重组与预测一致性相结合。两个窗口都接收完整的功率和运行状态监督。对于每个电器，仅当两个功率预测都满足固定可靠性标准且差异超过固定裕度时，才惩罚两者之间的不一致。所提出的方法使用具有两阶段共享到特定混合专家路由的多电器架构实现。在REDD、UK-DALE和REFIT数据集上，所提出的方法将电器平均绝对误差相对于单窗口训练分别从14.75 W降至13.14 W，从8.88 W降至8.51 W，从15.83 W降至14.55 W。标签保持的聚合重组和预测一致性仅在训练期间使用，不增加任何推理时模块或参数。
英文摘要
Non-intrusive load monitoring (NILM) estimates appliance power sequences from aggregate power, but models trained on source households commonly lose accuracy in unseen households. Aggregate power also contains loads from other appliances and measurement error, so predictions may depend on the residual background that co-occurs with source-household targets. Time-aligned submetered measurements and the additive decomposition of aggregate power expose a relation unused by window-wise supervision: an aggregate window can be recomposed by replacing only its residual background while preserving all modeled target-appliance power sequences pointwise. We combine label-preserving aggregate recomposition with prediction consistency. Both windows receive complete power and operating-state supervision. For each appliance, disagreement between the two power predictions is penalized only when both satisfy a fixed reliability criterion and only to the extent that it exceeds a fixed margin. The proposed method is implemented using a multi-appliance architecture with two-stage shared-to-specific mixture-of-experts routing. On REDD, UK-DALE, and REFIT, the proposed method lowers appliance-averaged mean absolute error relative to single-window training from 14.75 to 13.14 W, from 8.88 to 8.51 W, and from 15.83 to 14.55 W. Label-preserving aggregate recomposition and prediction consistency are used only during training, and add no inference-time module or parameter.
