Knowledge-Guided Multimodal Resource Identification in Low-Voltage Transformer Areas with Sparse Measurements

Abstract

Identifying distributed photovoltaic generation, battery energy storage, and load-dominant behaviour remains challenging when transformer-area measurements are sparse, operating patterns overlap, and reference labels are incomplete. We develop a knowledge-guided multimodal learning framework that combines raw electrical sequences with temporal, statistical, frequency-domain, and contextual descriptors. A knowledge-guided label library reconciles archived operating records, expert rules, and clustering-based screening, while transfer learning and WGAN-based augmentation are used to improve learning under limited and imbalanced data. The proposed framework jointly encodes raw electrical sequences, engineered descriptors, and contextual information and integrates their complementary representations through attention-based multimodal fusion. On a held-out real-only test set with independently verified labels, the proposed framework achieved 93.8% accuracy and a macro-F1 of 93.8%, while retaining 90.4% accuracy when 30% of the inputs were randomly masked. These results support further evaluation of knowledge-guided multimodal learning for transformer-area resource identification, although broader cross-region and cross-utility validation is required before operational deployment.