Latent Tensor Factorization with Nonlinear PID Control for Missing Data Recovery in Non-Intrusive Load Monitoring

• URL: http://arxiv.org/abs/2504.13483v1
• Date: Fri, 18 Apr 2025 05:48:14 GMT
• Title: Latent Tensor Factorization with Nonlinear PID Control for Missing Data Recovery in Non-Intrusive Load Monitoring
• Authors: Yiran Wang, Tangtang Xie, Hao Wu,
• Abstract summary: Non-Intrusive Load Monitoring (NILM) has emerged as a key smart grid technology.<n>This paper proposes a Proportional-integral-derivative (PID)-Incorporated Latent factorization of tensors (NPIL) model with two-fold ideas.<n> Experimental results on real-world NILM datasets demonstrate that the proposed NPIL model surpasses state-of-the-art models in convergence rate and accuracy when predicting the missing NILM data.
• Score: 2.94258758663678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-Intrusive Load Monitoring (NILM) has emerged as a key smart grid technology, identifying electrical device and providing detailed energy consumption data for precise demand response management. Nevertheless, NILM data suffers from missing values due to inescapable factors like sensor failure, leading to inaccuracies in non-intrusive load monitoring. A stochastic gradient descent (SGD)-based latent factorization of tensors model has proven to be effective in estimating missing data, however, it updates a latent factor solely based on the current stochastic gradient, without considering past information, which leads to slow convergence of anLFT model. To address this issue, this paper proposes a Nonlinear Proportional-integral-derivative (PID)-Incorporated Latent factorization of tensors (NPIL) model with two-fold ideas: a) rebuilding the instant learning error according to the principle of a nonlinear PID controller, thus, the past update information is efficiently incorporated into the learning scheme, and b) implementing gain parameter adaptation by utilizing particle swarm optimization (PSO) algorithm, hence, the model computational efficiency is effectively improved. Experimental results on real-world NILM datasets demonstrate that the proposed NPIL model surpasses state-of-the-art models in convergence rate and accuracy when predicting the missing NILM data.

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