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Chang Su
School of Electric and Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China

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Journal article
Published: 30 August 2019 in Applied Sciences
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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.

ACS Style

Jinying Yu; Yuchen Gao; Yuxin Wu; Dian Jiao; Chang Su; Xin Wu. Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration. Applied Sciences 2019, 9, 3558 .

AMA Style

Jinying Yu, Yuchen Gao, Yuxin Wu, Dian Jiao, Chang Su, Xin Wu. Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration. Applied Sciences. 2019; 9 (17):3558.

Chicago/Turabian Style

Jinying Yu; Yuchen Gao; Yuxin Wu; Dian Jiao; Chang Su; Xin Wu. 2019. "Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration." Applied Sciences 9, no. 17: 3558.