Non-intrusive load monitoring method based on the time-segmented state probability. (July 2022)

• Record Type: Journal Article
• Authors: Zhou, Yifei; Li, Fangshuo; Liu, Lina; Wang, Tao; Cheng, Zhijiong; Li, Ruichao; Gao, Jun
• Is Part Of: Energy reports. Volume 8(2022)Supplement 4
• Page Start: 1418
• Page End: 1423
• Publication Date: 2022-07
• Subjects: Non-intrusive load monitoring (NILM) -- Time-segmented state probability -- Smart grid -- Low-frequency data; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.02.021
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Appliance-level data is important for developing flexible two-way interactions between users and smart grids. Non-intrusive load monitoring (NILM) is a better way to obtain appliance power consumption information. Algorithms are used to decompose customers' total electricity consumption data into electricity consumption data of various appliances. In order to realize real-time load identification, a load identification method is proposed based on the operating probability of load in different periods. During the training phase, historical data is used to count the probability of the device being in various states at various time periods. Then, in the load decomposition stage, several appliances state estimation matrices are generated using the time-segmented state probability, and the performance function selects the optimal matrix as the identification result of the appliance state. Finally, the proposed algorithm is tested on the low-frequency dataset, and the test results verified that the load status recognition accuracy is more than 96%, which meets the application requirements of NILM.