Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

Applied Sciences

Xin Wu¹  Chang Su¹  Jinying Yu¹  Dian Jiao¹  Yuchen Gao¹  Yuxin Wu¹ 

[1] School of Electric and Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China;

关键词: non-intrusive load monitoring;    multi-feature;    linear classifier;    demand response;   

DOI  :  10.3390/app9173558

来源: DOAJ

【 摘 要 】

Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.

【 授权许可】

Unknown