Non-Intrusive Load Identification Method Based on KPCA-IGWO-RF

Sheng Hu, Gongjin Yuan, Kaifeng Hu, Cong Liu and Minghu Wu ()
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Sheng Hu: School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Gongjin Yuan: School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Kaifeng Hu: School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Cong Liu: School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Minghu Wu: School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China

Energies, 2023, vol. 16, issue 12, 1-14

Abstract: Non-invasive load monitoring (NILM) represents a crucial technology in enabling smart electricity consumption. In response to the challenges posed by high feature redundancy, low identification accuracy, and the high computational costs associated with current load identification models, a novel load identification model based on kernel principal component analysis (KPCA) and random forest (RF) optimized by improved Grey Wolf Optimizer (IGWO) is proposed. Initially, 17 steady-state load characteristics were selected as discrimination indexes. KPCA was subsequently employed to reduce the dimension of the original data and diminish the correlation between the feature indicators. Then, the dimension reduction in load data was classified by RF. In order to improve the performance of the classifier, IGWO was used to optimize the parameters of the RF classifier. Finally, the proposed model was implemented to identify 25 load states consisting of seven devices. The experimental results demonstrate that the identification accuracy of this method is up to 96.8% and the Kappa coefficient is 0.9667.

Keywords: non-invasive load identification; kernel principal component analysis; Grey Wolf Optimizer; random forest (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
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