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

TL;DR

This paper introduces a PID-controlled non-negative tensor factorization model to effectively impute missing data in NILM, improving accuracy and stability over existing methods for better energy management.

Contribution

It presents a novel PNLF model with dynamic parameter adjustment, enhancing tensor completion performance in NILM datasets with missing data.

Findings

PNLF outperforms existing tensor completion models in accuracy.

PNLF demonstrates higher efficiency in data imputation tasks.

The model improves load disaggregation and energy management accuracy.

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.