AI 中文总结
针对工业NILM的挑战，本研究提出SEDR-Seq2P模型，在IMDELD基准测试中较Seq2Point优化了多项指标，且推理延迟远低于WaveNet，实现了更优的准确率-延迟平衡，适配工业部署需求。
AI 中文摘要
工业非侵入式负荷监测（NILM）仍面临挑战，因为测量噪声和广泛存在的多台机器并发运行会降低在住宅数据上训练的模型的泛化能力。本研究采用一对多的多任务分解设置，即单个网络从总功率中估算多台工业机器的负荷。在IMDELD的统一评估协议下，本研究使用能量估算指标和准确率-延迟准则对Seq2Seq、Seq2SubSeq、Seq2Point、GRU和WaveNet进行基准测试。Seq2Point比Seq2Seq、Seq2SubSeq实现了更优的准确率-延迟平衡，而GRU和WaveNet虽准确率更高，但计算成本显著更高。为缩小这一差距，本研究提出SEDR-Seq2P，这是一种带有膨胀残差块和挤压-激励注意力的轻量型Seq2Point扩展模型。与Seq2Point基线相比，SEDR-Seq2P的平均绝对误差（MAE）降低约7%，决定系数提高约1%，匹配率提升约0.8%；与WaveNet相比，其推理延迟降低约58%，为可扩展的工业部署提供了良好的准确率-延迟权衡。
英文摘要
Industrial NILM remains challenging because measurement noise and widespread concurrent machine operation reduce the generalization of models tuned on residential data. This work adopts a one-to-many, multi-task disaggregation setting, in which a single network estimates multiple industrial machine loads from aggregate power. Under a unified evaluation protocol on IMDELD, we benchmark Seq2Seq, Seq2SubSeq, Seq2Point, GRU, and WaveNet using energy-estimation metrics and the accuracy-delay criterion. While Seq2Point offers a stronger accuracy-delay balance than Seq2Seq/Seq2SubSeq, GRU and WaveNet achieve higher accuracy at markedly higher computational cost. To close this gap, we propose SEDR-Seq2P, a lightweight Seq2Point extension with dilated residual blocks and squeeze-and-excitation attention. Relative to the Seq2Point baseline, SEDR-Seq2P reduces MAE by approximately 7%, improves the coefficient of determination by approximately 1%, and increases the match rate by approximately 0.8%. In addition, compared to WaveNet, SEDR-Seq2P reduces inference latency by approximately 58%, yielding a favorable accuracy-delay trade-off for scalable industrial deployment.
