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Paper ThuS1T3.3

Jrhilifa, ismael (ERERA, National High School of Arts and Crafts, Mohammed V Unive), Ouadi, Hamid (Ismra), Jilbab, Abdelilah (Mohammed V University in Rabat), Mounir, Nada (ERERA, ENSAM, Mohammed V University, Rabat, Morocco), GHEOUANY, Saad (ERERA, National School of Arts and Crafts, Mohammed V University), EL BAKALI, Saida (ERERA, ENSAM, Mohammed V University, Rabat, Morocco)

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

Scheduled for presentation during the Regular Session "Optimal energy scheduling for residential buildings" (ThuS1T3), Thursday, July 11, 2024, 09:40−10:00, Session room 3

12th IFAC Symposium on Control of Power & Energy Systems, July 10-12, 2024, Rabat, Morocco

This information is tentative and subject to change. Compiled on January 2, 2025

Keywords Artificial Intelligence in Smart Grids, Intelligent Energy Management Systems and Digital Twins, Optimization in Energy Systems

Abstract

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.

 

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