A Novel Transfer Learning-Based Intelligent Nonintrusive Load-Monitoring With Limited Measurements

In this article, the real-time nonintrusive load-monitoring (NILM) problem with limited measurements, i.e., low sampling rate data, is investigated. NILM is a technique to identify the various types of appliances by analyzing the voltage and current features collected by sensors, such as smart outlets. It is one of the most important topics in smart grid management and optimization. Although recent NILM methods developed several general models that can be used for various scenarios, these algorithms either require high-frequency measurement devices or have been limited to use for some specific appliances. A real-time intelligent NILM algorithm that is able to be transferred among different appliances with low-frequency data is still lacking and desperately needed. In this article, a novel online learning-based intelligent NILM algorithm has been developed that can, online, infer a variety of appliances using a transferred model with limited measurements. Especially, the developed algorithm integrates the emerging transfer learning technique along with deep neural networks. Two neural networks are used in two different stages: 1) the long short-term memory (LSTM) neural network to extract the lower level spatial and temporal features from the gray-scale image generated by the measurements and 2) the probabilistic neural network (PNN) to classify the appliance type as well as transfer between appliances. Finally, the algorithm is implemented into practical smart outlet hardware to demonstrate its effectiveness.