Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 17 June 2019 / Approved: 18 June 2019 / Online: 18 June 2019 (06:06:23 CEST)

A peer-reviewed article of this Preprint also exists.

Yu, J.; Gao, Y.; Wu, Y.; Jiao, D.; Su, C.; Wu, X. Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration. Appl. Sci. 2019, 9, 3558. Yu, J.; Gao, Y.; Wu, Y.; Jiao, D.; Su, C.; Wu, X. Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration. Appl. Sci. 2019, 9, 3558.

Journal reference: Appl. Sci. 2019, 9, 3558
DOI: 10.3390/app9173558

Abstract

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 decompose the whole loads in a household, which leads to low identification accuracy. In this paper, an NILM approach based on multi-feature integrated classification (MFIC) is explored, which combines some non-electrical features such as ON/OFF duration, usage frequency of appliances, and usage period to improve load differentiability. The implementation of MFIC algorithm is consistent with traditional event-based method. 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. Simulation results using an open-access dataset demonstrate the effectiveness and high accuracy of MFIC algorithm, with the state-of-the-art NILM methods as benchmarks.

Subject Areas

non-intrusive load monitoring; load disaggregation; linear classifier; demand response

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