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.
Highlights
There are three aspects to the key technologies associated with this: advanced metering infrastructure (AMI) standards, systems, and terminal technologies; intelligent two-way interactive operation mode and supporting techniques; and the interaction between the user’s electrical environment and energy consumption patterns
Nonintrusive load monitoring (NILM), which is a vital part of smart power utilization techniques, can achieve fine-grained tracking of energy consumption and provide load disaggregation information without any intrusive device installation
We introduced a linear-chain conditional random fields (CRFs) model for load disaggregation and demonstrated that this graphical model is feasible for a NILM task
Summary
As the core of an interactive service system, smart power utilization is one of the essential components of a smart grid. Load monitoring can improve the power information collection system and intelligent power system and support two-way interactive service and smart power utilization. Nonintrusive load monitoring (NILM), which is a vital part of smart power utilization techniques, can achieve fine-grained tracking of energy consumption and provide load disaggregation information without any intrusive device installation. These data can be further applied to optimize energy conservation strategies
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