To optimize energy utilization, efficient energy management practices are important. Non-intrusive load monitoring (NILM) has emerged as a promising solution, particularly with the advent of deep learning techniques.

This paper introduces a novel approach to NILM: Self-Adaptive Non-Intrusive Load Monitoring using Deep Learning. Conventional NILM models often struggle to adapt to changes in power consumption patterns, especially with aging appliances.

We propose a Self-Adaptive NILM model that integrates deep learning techniques with transfer learning and pseudolabeling.

W. A. Yasodya, S. M. L. Arampola, M. S. K. Nisakya, V. Logeeshan, S. Kumarawadu, C. Wanigasekara.

This research addresses the critical challenge of appliance aging in Non-Intrusive Load Monitoring (NILM) systems through innovative deep learning approaches.