Simultaneous Load Identification Method Based on Hybrid Features and Genetic Algorithm for Nonintrusive Load Monitoring

Nonintrusive load monitoring (NILM) is a widely accepted technology to conduct load monitoring. Many effective methods have been established to make NILM more practical. However, the focus of current methods is mainly on the identification accuracy and efficiency of single load under the individual...

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Bibliographic Details
Published in:Mathematical problems in engineering 2022, Vol.2022, p.1-13
Main Authors: Yi, Shu-Hui, Wang, Jian, Liu, Jun-Jie
Format: Article
Language:English
Subjects:
Accuracy
Appliances
Consumption
Cost control
Deep learning
Efficiency
Electricity
Feature extraction
Genetic algorithms
Household appliances
Identification
Identification methods
Integer programming
Linear programming
Machine learning
Methods
Monitoring
Optimization
Optimization models
Reactive power
Wavelet transforms
Weighting
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Summary:Nonintrusive load monitoring (NILM) is a widely accepted technology to conduct load monitoring. Many effective methods have been established to make NILM more practical. However, the focus of current methods is mainly on the identification accuracy and efficiency of single load under the individual appliance operated independently, which have limited support for the identification problem under multiple appliances operated simultaneously. Therefore, a simultaneous identification method is proposed to efficiently identify the total load under multiple appliances operated simultaneously in this paper. The proposed identification method mainly consists of three parts: hybrid features extraction, simultaneous identification optimization model construction, and frequency-weighting-factor-based genetic algorithm (FWF-GA). Firstly, the hybrid feature model, which integrates the features of active power, reactive power, and harmonic magnitude, is constructed by hybrid features extraction. Secondly, the simultaneous identification optimization model is constructed by employing the features of active and reactive power. Thirdly, the developed FWF-GA is used to solve the simultaneous identification optimization problem. In FWF-GA, the relative errors of active power, reactive power, and the frequency-weighting factor of harmonic magnitude are used to evaluate the fitness of an individual. Finally, a NILM practice to identify household appliances is used to demonstrate the validity of the proposed method.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/7876380