Non-Intrusive Load Monitoring with Missing Data Imputation Based on
  Tensor Decomposition
DengYu Shi
With the widespread adoption of Non-Intrusive Load Monitoring (NILM) in
building energy management, ensuring the high quality of NILM data has become
imperative. However, practical applications of NILM face challenges associated
with data loss, significantly impacting accuracy and reliability in energy
management. This paper addresses the issue of NILM data loss by introducing an
innovative tensor completion(TC) model- Proportional-Integral-Derivative
(PID)-incorporated Non-negative Latent Factorization of Tensors (PNLFT) with
twofold ideas: 1) To tackle the issue of slow convergence in Latent
Factorization of Tensors (LFT) using Stochastic Gradient Descent (SGD), a
Proportional-Integral-Derivative controller is introduced during the learning
process. The PID controller utilizes historical and current information to
control learning residuals. 2) Considering the characteristics of NILM data,
non-negative update rules are proposed in the model's learning scheme.
Experimental results on three datasets demonstrate that, compared to
state-of-the-art models, the proposed model exhibits noteworthy enhancements in
both convergence speed and accuracy.