Weakly Supervised Transfer Learning for Multi-label Appliance Classification

Non-intrusive Load Monitoring refers to the techniques for providing detailed information on appliances’ states or their energy consumption by measuring only aggregate electrical parameters. Supervised deep neural networks have reached the state-of-the-art in this task, and to improve the performance when training and test data domains differ, transfer learning techniques have been successfully applied. However, these techniques rely on data labeled sample-by-sample (strong labels) to be effective, which can be particularly costly in transfer learning since it requires collecting and annotating data in the target domain. To mitigate this issue, this work proposes a cross-domain transfer learning approach based on weak supervision and Convolutional Recurrent Neural Networks for multi-label appliance classification. The proposed method is based on the concept of inexact supervision by modeling NILM as a Multiple Instance Learning problem, exploiting different and less costly annotations called weak labels. The learning strategy is able to exploit weak labels both for pre-training and fine-tuning the models. UK-DALE and REFIT are used in the experiments as source and target domain datasets to train, fine-tune, and evaluate the networks. The results demonstrate the effectiveness of the proposed method compared to the related pre-trained models. In particular, when the model is pre-trained on strongly and weakly labeled data of UK-DALE and then fine-tuned only on REFIT weak labels, the performance improves by 20.3%.