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Open AccessArticle

A Novel Nonintrusive Load Monitoring Approach based on Linear-Chain Conditional Random Fields

School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Energies 2019, 12(9), 1797; https://doi.org/10.3390/en12091797
Received: 28 March 2019 / Revised: 7 May 2019 / Accepted: 7 May 2019 / Published: 11 May 2019
In a real interactive service system, a smart meter can only read the total amount of energy consumption rather than analyze the internal load components for users. Nonintrusive load monitoring (NILM), as a vital part of smart power utilization techniques, can provide load disaggregation information, which can be further used for optimal energy use. In our paper, we introduce a new method called linear-chain conditional random fields (CRFs) for NILM and combine two promising features: current signals and real power measurements. The proposed method relaxes the independent assumption and avoids the label bias problem. Case studies on two open datasets showed that the proposed method can efficiently identify multistate appliances and detect appliances that are not easily identified by other models. View Full-Text
Keywords: load disaggregation; nonintrusive load monitoring; conditional random fields; feature extraction load disaggregation; nonintrusive load monitoring; conditional random fields; feature extraction
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MDPI and ACS Style

He, H.; Liu, Z.; Jiao, R.; Yan, G. A Novel Nonintrusive Load Monitoring Approach based on Linear-Chain Conditional Random Fields. Energies 2019, 12, 1797.

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