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

Hui He, Zixuan Liu, Runhai Jiao and Guangwei Yan
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Hui He: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Zixuan Liu: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Runhai Jiao: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Guangwei Yan: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2019, vol. 12, issue 9, 1-17

Abstract: 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.

Keywords: load disaggregation; nonintrusive load monitoring; conditional random fields; feature extraction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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