Core Concepts
The author introduces a PID-incorporated Non-negative Latent Factorization of Tensors (PNLFT) model to address missing data in Non-Intrusive Load Monitoring (NILM) efficiently.
Abstract
The paper addresses the challenges associated with data loss in NILM by proposing a PNLFT model. It incorporates a PID controller and non-negative update rules to enhance convergence speed and accuracy. Experimental results demonstrate significant improvements over existing models.
Key Points:
Introduction of PNLFT model for NILM data imputation.
Utilization of PID controller and non-negative update rules.
Demonstrated enhancements in convergence speed and accuracy through experiments on three datasets.
The proposed PNLFT model shows promising results in restoring missing data in NILM, offering efficient solutions to the challenges of data loss.
Stats
"Experimental results indicate that compared to state-of-the-art models, the proposed model exhibits noteworthy enhancements in both convergence speed and accuracy."
"RMSE values of 0.1250, 0.2302, and 0.3655 for D1, D2, and D3 respectively were achieved by the PNLFT model."