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

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 accura...

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Bibliographic Details
Published inApplied sciences Vol. 9; no. 17; p. 3558
Main Authors Yu, Jinying, Gao, Yuchen, Wu, Yuxin, Jiao, Dian, Su, Chang, Wu, Xin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2019
Subjects
Algorithms
Appliances
Benchmarks
Consumption
demand response
Direct reduction
Disaggregation
Electric appliances
Electricity distribution
Energy conservation
Households
linear classifier
Load
Methods
multi-feature
non-intrusive load monitoring
Power consumption
Power supply
Segmentation
Signal processing
Similarity
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9173558