Non-intrusive load monitoring algorithms for privacy mining in smart grid

Non-intrusive load monitoring (NILM) method is essentially artificial intelligence algorithms for energy conservation and privacy mining. It obtains consumers' privacy data by decomposing aggregated meter readings of consumer energy consumption into the individual devices energy consumption. In this chapter, we first introduce the background and the advantage of the NILM method, and the classification of NILM method. Secondly, we demonstrate the general process of NILM method. The specific process contains data preprocess, event detection and feature extraction, and energy consumption learning and appliance inference. Furthermore, we introduce a supervisedNILM example and an unsupervised example. We describe their processes, and discuss their characteristics and performances. In addition, the applications of NILM method are depicted. Lastly, we conclude this chapter and give the future work.