Non-Intrusive Load Monitoring with Missing Data Imputation Based on Tensor Decomposition

arXiv:2403.07012v1 Announce Type: new
Abstract: 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 …

abstract accuracy adoption applications arxiv become building challenges cs.lg data data loss energy energy management face however imputation loss management monitoring paper practical quality reliability tensor type