Unknown Appliances Detection for Non-Intrusive Load Monitoring Based on Conditional Generative Adversarial Networks
Yinghua Han, Keke Li, Chen Wang, Fangyuan Si, Qiang Zhao
Abstract
Non-intrusive load monitoring (NILM) provides fine-grained consumption information at the appliance level by analyzing the terminal voltage and total current measured. It shows prospective applications in demand side management, such as demand response, energy efficiency, and home energy management system. However, most cutting-edge NILM models have a critical assumption that switching events are triggered by known appliances in the training set, which may be unrealistic. In reality, new appliances are constantly being added, reducing the effectiveness of current methods for load monitoring. In this paper, we propose a conditional generative adversarial network (CGAN) to correctly classify all the known appliances while simultaneously detecting unknown ones using the V-I trajectory features. We integrate variational autoencoder and capsule networks in the generator network, the capsule features of the same known appliance class are forced to match a pre-defined Gaussian and a group of Gaussian priors (one for each appliance class) as the posterior distribution approximately. Furthermore, using an additional encoder network maps the generated V-I trajectory to its latent representation, minimizing the distance between the latent representations and the feature vectors of the generator aids in learning the data distribution for known appliance samples during the training process. In these ways, we can improve the ability of the model to detect unknown appliances. Experimental results on two public datasets demonstrate the effectiveness and superiority of our method.