A semi-supervised contrastive learning framework based on current signals for non-intrusive load monitoring

Abstract

Non-intrusive load monitoring (NILM) improves energy efficiency and mitigates the greenhouse effect by effectively monitoring energy consumption. However, many methods fail to fully utilize raw signal information and require large amounts of labeled data for training. In this paper, we propose a novel method for NILM. First, raw load data are transformed into two views using strong and weak augmentations, with data locality enhanced through patching to capture more comprehensive load features. Next, an unsupervised contrastive learning framework is designed based on patch and context contrast, aiming to maximize similarity between different views of the same sample while minimizing similarity between different samples. This framework enables the encoder to learn effective feature representations from unlabeled load samples. Then, the learned representations are used by a classifier for load identification, relying on a small amount of labeled data for supervised training. Ultimately, the paper achieves a semi-supervised model that combines a self-supervised encoder with a classifier guided by partial label information to enhance NILM performance. Experiments on PLAID, WHITED and REDD public datasets demonstrate the effectiveness of the proposed algorithm, which outperforms comparative algorithms in performance.