Unsupervised Domain Adaptation for Nonintrusive Load Monitoring Via Adversarial and Joint Adaptation Network

Abstract

Nonintrusive load monitoring (NILM) is a technique to disaggregate an appliance's load consumption from the aggregate load in a house. Monitoring the energy behavior has become increasingly important for home energy management. For many machine learning-based models, model training needs enough, and diverse appliance-level labeled data from different houses, which is very time-consuming, expensive, and unacceptable for users. In this article, we propose an algorithm based on the adversarial network and the joint adaptation network for energy disaggregation to decrease the distribution gaps of both the feature space and the label space between the source and target domains. With only very limited labeled data in the source domain and enough unlabeled data in the target domain, our proposed algorithm can obtain satisfactory accuracy results for NILM. Extensive experiments for intradomain and interdomain demonstrate that the proposed algorithm can significantly improve the domain adaptation. Comparing with the baseline method that without any domain adaptation, the improvement on mean absolute error with the proposed algorithm can reach 67.72%, 67.53%, and 66.56% for the washing machine (W.M), the dishwasher (D.W), and the microwave (M.V), respectively.