论文标题
Conv-Nilm-NET，一种用于能源分离的因果和多种选择模型
Conv-NILM-Net, a causal and multi-appliance model for energy source separation
论文作者
论文摘要
非侵入性负载监控（NILM）试图通过从单个骨料测量中估算单个设备功率来节省能源。深层神经网络在尝试解决尼尔姆问题方面变得越来越受欢迎。但是，大多数使用的模型用于负载识别，而不是在线源分离。在源分离模型中，大多数使用单件任务学习方法，其中神经网络仅针对每个设备进行培训。该策略在计算上是昂贵的，并且忽略了多个电器可以同时活跃的事实以及它们之间的依赖性。其余模型不是因果关系，这对于实时应用很重要。受到语音分离模型Convtas-Net的启发，我们提出了Conv-Nilm-Net，这是端到端尼尔姆的完全卷积框架。 conv-nilm-net是多个设备源分离的因果模型。我们的模型在两个真正的数据集和英国大放异彩上进行了测试，并且显然超过了最新技术的状态，同时保持尺寸明显小于竞争模型。
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.
