2016/10/04 by Christoph Klemenjak, Klemenjak, Christoph, Peter Goldsborough +1
Engineering · #FOS: Computer and information sciences #Green IT and Sustainability #IoT-based Smart Home Systems #Other Computer Science (cs.OH) #Smart Grid Energy Management
paper · pdf · doi:10.48550/arxiv.1610.01191
openalex publication_date 2016/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the roll-out of smart meters the importance of effective non-intrusive load monitoring (NILM) techniques has risen rapidly. NILM estimates the power consumption of individual devices given their aggregate consumption. In this way, the combined consumption must only be monitored at a single, central point in the household, providing various advantages such as reduced cost for metering equipment. In this paper we discuss the fundamental building-blocks of NILM, first giving a taxonomy of appliance models and device signatures and then explaining common supervised and unsupervised learning methods. Furthermore, we outline a fundamental algorithm that tackles the task of NILM. Subsequently, this paper reviews recent research that has brought novel insight to the field and more effective techniques. Finally, we formulate future challenges in the domain of NILM and smart meters.