Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique. To mitigate the unidentifiable problem, various methods incorporating domain knowledge into NILM have been proposed and shown effective experimentally. Recently, among these methods, deep neural networks are shown performing best. Arguably, the recently proposed sequence-to-point (seq2point) learning is promising for NILM. However, the results were only carried out on the same data domain. It is not clear if the method could be generalised or transferred to different domains, e.g., the test data were drawn from a different country comparing to the training data. We address this issue in the paper, and two transfer learning schemes are proposed, i.e., appliance transfer learning (ATL) and cross-domain transfer learning (CTL). For ATL, our results show that the latent features learnt by a `complex' appliance, e.g., washing machine, can be transferred to a `simple' appliance, e.g., kettle. For CTL, our conclusion is that the seq2point learning is transferable. Precisely, when the training and test data are in a similar domain, seq2point learning can be directly applied to the test data without fine tuning; when the training and test data are in different domains, seq2point learning needs fine tuning before applying to the test data. Interestingly, we show that only the fully connected layers need fine tuning for transfer learning. Source code can be found at https://github.com/MingjunZhong/transferNILM.

中文翻译：

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非侵入式负载监控的转移学习

非侵入式负载监控（NILM）是一种仅从家庭中记录的主电源中恢复源设备的技术。NILM是无法识别的，因此是一个挑战性的问题，因为仅给出电源的设备的推定功率值就不可能唯一。为了减轻无法识别的问题，已经提出了将领域知识整合到NILM中的各种方法，并通过实验证明了它们的有效性。最近，在这些方法中，深度神经网络显示出最佳的性能。可以说，最近提出的序列到点（seq2point）学习对于NILM很有前途。但是，结果仅在同一数据域上执行。尚不清楚该方法是否可以推广或转移到不同的领域，例如，与培训数据相比，测试数据来自不同国家。我们在本文中解决了这个问题，并提出了两种转移学习方案，即设备转移学习（ATL）和跨域转移学习（CTL）。对于ATL，我们的结果表明，“复杂”设备（例如洗衣机）学习到的潜在功能可以转移到“简单”设备（例如水壶）中。对于CTL，我们的结论是seq2point学习是可移植的。准确地说，当训练和测试数据位于相似的域中时，无需进行微调就可以将seq2point学习直接应用于测试数据。当训练和测试数据位于不同的域中时，需要先对seq2point学习进行微调，然后再应用于测试数据。有趣的是，我们表明只有完全连接的层才需要对转移学习进行微调。可以在https：// github上找到源代码。