Details

Autor(en) / Beteiligte

• Zhou, Yifei
• Li, Fangshuo
• Liu, Lina
• Wang, Tao
• Cheng, Zhijiong
• Li, Ruichao
• Gao, Jun

Titel

Non-intrusive load monitoring method based on the time-segmented state probability

Ist Teil von

• Energy reports, 2022-07, Vol.8, p.1418-1423

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.