An Overview of Non-Intrusive Load Monitoring Based on V-I Trajectory Signature

Jiangang Lu, Ruifeng Zhao (), Bo Liu (), Zhiwen Yu, Jinjiang Zhang and Zhanqiang Xu
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Jiangang Lu: Electric Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Ruifeng Zhao: Electric Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Bo Liu: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Zhiwen Yu: Electric Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510600, China
Jinjiang Zhang: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Zhanqiang Xu: Electric Power Dispatching and Control Center, Guangdong Power Grid Co., Ltd., Guangzhou 510600, China

Energies, 2023, vol. 16, issue 2, 1-15

Abstract: Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information of each appliance by analyzing the voltage and current data measured at a single point on the bus, which is of great significance for promoting and improving the efficiency and sustainability of the power grid and enhancing the energy efficiency of users. NILM mainly includes data collection and preprocessing, event detection, feature extraction, and appliance identification. One of the most critical steps in NILM is signature extraction, which is the basis for all algorithms to achieve good state detection and energy disaggregation. With the generalization of machine learning algorithms, different algorithms have also been used to extract unique signatures of appliances. Recently, the development and deployment of the voltage–current (V-I) trajectory signatures applied for appliance identification motivated us to present a comprehensive review in this domain. The V-I trajectory signatures have the potential to be an intermediate domain between computer vision and NILM. By identifying the V-I trajectory, we can detect the operating state of the appliance. We also summarize existing papers based on V-I trajectories and look forward to future research directions that help to promote the field’s development.

Keywords: non-intrusive load monitoring; load identification; voltage–current trajectory; deep learning (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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