VMD-GRU Based Non-Intrusive Load Monitoring For Home Energy Management System

This paper proposes a solution for Non-Intrusive Load Monitoring (NILM) in residential energy systems. The presented solution offers substantial benefits for load shifting tasks and home energy management systems, enabling effective load management and optimization. The proposed approach combines Va...

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Bibliographic Details
Published in:IFAC-PapersOnLine 2024, Vol.58 (13), p.176-181
Main Authors: Jrhilifa, Ismael, Ouadi, Hamid, Jilbab, Abdelilah, Gheouany, Saad, Mounir, Nada, El Bakali, Saida
Format: Article
Language:English
Subjects:
Deep Learning
Home Energy Management system
Non Intrusive Load Monitoring
Sustainable Power Consumption
Variational Mode Decomposition
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Summary:This paper proposes a solution for Non-Intrusive Load Monitoring (NILM) in residential energy systems. The presented solution offers substantial benefits for load shifting tasks and home energy management systems, enabling effective load management and optimization. The proposed approach combines Variable Mode Decomposition (VMD) and Gated Recurrent Units Network (GRU). VMD parameter selection is done through the Salp Swarm Algorithm (SSA). Correlation analysis is used to identify the most relevant Intrinsic Mode Functions (IMFs) for each appliance. A comparison is made with MILD without VMD to demonstrate the impact of VMD on disaggregation performance. The proposed strategy yielded significant results, with an average RMSE of 4.10 (W) and an average MAPE of 4.14%. In contrast, the other approach had an average RMSE of 12.79 (W) and an average MAPE of 6.40%. These findings indicate that the proposed model performs better in disaggregation tasks.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2024.07.479