Non-Intrusive Load Monitoring Model Based on SimCLR and Visualized Color V-I Trajectories

Current non-intrusive load monitoring (NILM) methods rely on large amounts of labeled historical data and face domain shift issues, which limits the application of deep learning models in practical scenarios. To this end, this paper proposes a SimCLR-ADA-LM framework based on visualized color V-I trajectories. Initially, unlabeled load data from the source domain (PLAID) and target domain (WHITED) are converted into RGB color V-I trajectories and input into the model. The framework enhances intra-class aggregation through contrastive learning and achieves inter-domain feature alignment via adversarial training between the encoder and the domain discriminator to obtain domain-invariant features. Subsequently, the model is fine-tuned using a small amount of labeled data from the target domain to achieve load identification. Ablation and comparative experimental results demonstrate that the proposed model exhibits superior performance advantages over conventional models in cross-domain identification tasks. Furthermore, it maintains significant learning efficiency and recognition robustness even under conditions of limited labeled data.