RTNILM: A Deep Robust Transfer Neural Network for Practical Application of NILM

Nonintrusive load monitoring (NILM) has emerged as a pivotal technology in energy management, garnering significant attention in both research and engineering communities. Despite its potential, conventional NILM methods often exhibit limitations in addressing critical challenges such as domain shift, new appliance detection, and noise interference, thereby hindering their practical application. To overcome these limitations, we propose meta-learning named deep robust transfer neural network (RTNILM), a novel framework that simultaneously addresses these challenges while significantly enhancing NILM performance in real-world scenarios. The RTNILM framework incorporates several innovative components: first, an optimized deep, wide, and robust network architecture is derived through neural architecture search from source domain data set; second, pretrained with optimized general end-to-end loss to acquire a general appliance recognition ability and enhance the model’s robustness; third, further trained with model-agnostic meta-learning strategy to improve the model’s generalization on the target domain data set; fourth, by comparing the similarities between features from new appliances and known appliances, achieve new appliance detection. Extensive experimental evaluations across three public datasets and one self-collected dataset demonstrate the superiority of RTNILM in cross-domain recognition, new appliance detection, and noise interference. The average improvement in accuracy of cross domain appliance recognition, new appliance detection, and noise interference compared to other methods exceeded 10%, 20%, and 5%, respectively.