Real-time non-intrusive load monitoring: A light-weight and scalable approach

Abstract

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 solutions so far focus on a predefined number of home appliances, this paper proposes a multi-class NILM system which can detect in real-time any number of appliances and can be efficiently embedded into simple microprocessors. The key feature for the identification of the appliances is the processing of measured turn-on active power transient responses sampled at 100 Hz. The NILM system includes three stages; adaptive thresholding event detection method, 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.