A PID-Controlled Non-Negative Tensor Factorization Model for Analyzing Missing Data in NILM

• URL: http://arxiv.org/abs/2403.07012v2
• Date: Mon, 21 Oct 2024 14:53:11 GMT
• Title: A PID-Controlled Non-Negative Tensor Factorization Model for Analyzing Missing Data in NILM
• Authors: DengYu Shi,
• Abstract summary: Non-Intrusive Load Monitoring (NILM) has become an essential tool in smart grid and energy management. Traditional imputation methods, such as linear and matrix factorization, struggle with nonlinear relationships and are sensitive to sparse data. This paper proposes a Proportional-Integral-Derivative (PID) Non-Negative Latent Factorization of tensor (PNLF) model, which dynamically adjusts parameter gradients to improve convergence, stability, and accuracy.
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• Abstract: With the growing demand for energy and increased environmental awareness, Non-Intrusive Load Monitoring (NILM) has become an essential tool in smart grid and energy management. By analyzing total power load data, NILM infers the energy usage of individual appliances without the need for separate sensors, enabling real-time monitoring from a few locations. This approach helps users understand consumption patterns, enhance energy efficiency, and detect anomalies for effective energy management. However, NILM datasets often suffer from issues such as sensor failures and data loss, compromising data integrity, thereby impacting subsequent analysis and applications. Traditional imputation methods, such as linear interpolation and matrix factorization, struggle with nonlinear relationships and are sensitive to sparse data, resulting in information loss. To address these challenges, this paper proposes a Proportional-Integral-Derivative (PID) Controlled Non-Negative Latent Factorization of Tensor (PNLF) model, which dynamically adjusts parameter gradients to improve convergence, stability, and accuracy. Experimental results show that the PNLF model significantly outperforms state-of-the-art tensor completion models in both accuracy and efficiency. By addressing data loss issues, this study enhances load disaggregation precision and optimizes energy management, providing reliable data support for smart grid applications and policy formulation.

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