Next Article in Journal
3D Device for Forces in Swimming Starts and Turns
Previous Article in Journal
An Integrated Cognitive Radio Network for Coastal Smart Cities
Previous Article in Special Issue
A Priority-Based Synchronous Phasor Transmission Protocol Extension Method for the Active Distribution Network
Open AccessArticle

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

School of Electric and Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(17), 3558; https://doi.org/10.3390/app9173558
Received: 17 June 2019 / Revised: 27 July 2019 / Accepted: 26 August 2019 / Published: 30 August 2019
(This article belongs to the Special Issue Energy Management and Smart Grids)
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. View Full-Text
Keywords: non-intrusive load monitoring; multi-feature; linear classifier; demand response non-intrusive load monitoring; multi-feature; linear classifier; demand response
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop