Real-Time Non-Intrusive Load Monitoring: A Machine-Learning Approach for Home Appliance Identification

Non-intrusive load monitoring (NILM) is a topic that lately attracts both the academic and the industrial interest. NILM is used to reveal useful information regarding the consumption breakdown on appliance or activity level, thus can be a key solution to unlock various smart-home services and opportunities. To that end, deep learning has arisen as a prominent solution. Although most of the known NILM techniques so far focus on a predefined number of home appliances, this paper proposes a system which can detect in real-time any number of appliances. In the proposed solution switched-on appliances are identified by processing the measured active power transient response sampled at 100 Hz. The NILM system includes three stages; statistics-based event detection, convolutional neural network and k-nearest neighbors classifier. For future extensions, it is capable to automatically identify new appliances; thus, no retraining and additional modeling is required.