Non-Intrusive Load Monitoring (NILM) seeks to save energy by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become increasingly popular in attempting to solve NILM problems. However most used models are used for Load Identification rather than online Source Separation. Among source separation models, most use a single-task learning approach in which a neural network is trained exclusively for each appliance. This strategy is computationally expensive and ignores the fact that multiple appliances can be active simultaneously and dependencies between them. The rest of models are not causal, which is important for real-time application. Inspired by Convtas-Net, a model for speech separation, we propose Conv-NILM-net, a fully convolutional framework for end-to-end NILM. Conv-NILM-net is a causal model for multi appliance source separation. Our model is tested on two real datasets REDD and UK-DALE and clearly outperforms the state of the art while keeping a significantly smaller size than the competing models.

翻译：非侵入式负荷监测（NILM）旨在通过从单一总测量值中估算各家电的能耗来实现节能。深度神经网络在解决NILM问题中日益流行，然而，现有模型多用于负荷识别而非在线源分离。在源分离模型中，大多数采用单任务学习方法，即为每台家电单独训练一个神经网络。这种策略计算成本高昂，且忽略了多台家电可能同时运行及其相互依赖关系。其余模型则不具备因果性，而这对于实时应用至关重要。受语音分离模型ConvTas-Net启发，我们提出Conv-NILM-Net，一种用于端到端NILM的全卷积框架。Conv-NILM-Net是一种用于多家电源分离的因果模型。该模型在两个真实数据集REDD和UK-DALE上进行了测试，其在保持远小于竞争模型规模的同时，明显优于现有技术。