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
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
(This article belongs to the Special Issue Artificial Intelligence for Smart and Sustainable Energy Systems and Applications)
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.