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Practical applications of non-intrusive load monitoring (NILM) require load recognition models that generalize to unseen data from new houses and operate efficiently on edge devices. However, existing NILM approaches, particularly deep learning models, are computationally intensive and prone to performance degradation when deployed to new houses due to domain shifts. To address these challenges, this article proposes a Weighted Transferable Random Forest (WTRF) approach for load recognition. Based on the Random Forest (RF) framework, WTRF incorporates a transfer learning mechanism to swiftly adapt the model to new houses using only 1 to 3 labeled samples per appliance. The model is lightweight, with a memory size under 300 KB. Case studies on three datasets demonstrate its effectiveness, including a macro F1-score of 97.0 ± 2.6% when transferring from PLAID to WHITED, a significant improvement over 5.7±1.7% achieved by source-only models. Deployed on a Raspberry Pi 4, WTRF achieves update times as low as 3.1±0.3 seconds and testing times of approximately 3 ms per house. These results highlight WTRF’s efficiency in addressing domain shifts and its suitability for real-time NILM in resource-constrained edge environments.
Yan et al. (Wed,) studied this question.