Practical application of deep learning based non-intrusive load monitoring (NILM) system requires the deep neural network model to generalize on new unseen data. Existing NILM solutions are not suitable for real-world application due to their poor disaggregation accuracy on new unseen data. In order to address this problem, this paper presents a NILM algorithm that uses data augmentation to generate synthetic data for training deep convolutional neural network models for each target appliance. Proposed data augmentation technique works by combining on and off-durations of a target appliance from various datasets, and forms a unified and comprehensive synthetic aggregate and sub-meter profiles. Apart from proposed algorithm, this paper also proposes an evaluation approach that relies on total predicted energy and ground-truth energy of an appliance to provide detailed insights about total overlapping energy, missing energy and extra energy predicted by the algorithm. Comparison results on our proposed evaluation approach showed that proposed disaggregation algorithm was able to predict energy that was 60% overlapping with ground truth energy and 36% energy was extra. Overall results showed that overlapping energy was 2.5 times more, and extra-predicted energy was 60% less than state-of-the-art algorithms in unseen test cases.

中文翻译：

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使用数据增强的基于深度学习的非侵入式负荷监测系统的泛化性改进

基于深度学习的非侵入式负载监控 (NILM) 系统的实际应用需要深度神经网络模型来概括新的未知数据。现有的 NILM 解决方案不适合实际应用，因为它们对新的看不见的数据的分解精度很差。为了解决这个问题，本文提出了一种 NILM 算法，该算法使用数据增强来生成合成数据，用于为每个目标设备训练深度卷积神经网络模型。提议的数据增强技术通过结合来自各种数据集的目标设备的开启和关闭持续时间来工作，并形成统一和全面的合成聚合和子仪表配置文件。除了提出的算法，本文还提出了一种评估方法，该方法依赖于电器的总预测能量和地面真实能量，以提供有关算法预测的总重叠能量、缺失能量和额外能量的详细信息。我们提出的评估方法的比较结果表明，所提出的分解算法能够预测与地面真实能量重叠 60% 的能量和额外的 36% 能量。总体结果表明，在看不见的测试用例中，重叠能量是 2.5 倍，额外预测的能量比最先进的算法少 60%。我们提出的评估方法的比较结果表明，所提出的分解算法能够预测与地面真实能量重叠 60% 的能量和额外的 36% 能量。总体结果表明，在看不见的测试用例中，重叠能量是 2.5 倍，额外预测的能量比最先进的算法少 60%。我们提出的评估方法的比较结果表明，所提出的分解算法能够预测与地面真实能量重叠 60% 的能量和额外的 36% 能量。总体结果表明，在看不见的测试用例中，重叠能量是 2.5 倍，额外预测的能量比最先进的算法少 60%。