Fine-Tuning Non-Intrusive Load Monitoring Model Through User Interaction: A Practical Approach to Appliance Recognition with Limited Labeled Data

A novel fine-tuning method is introduced for Non-Intrusive Load Monitoring (NILM), using transfer learning to adapt pre-trained deep learning models for deferrable appliances with distinct short cycles (such as washing machines and dishwashers). This approach enhances model generalization by using limited user-labeled data with readily available, low-frequency aggregate consumption data from smart meters. The method eliminates the need for high-frequency sampling or intrusive sub-metering, and high accuracy in deployable NILM applications. The results show that high accuracy in appliance state recognition is achieved with minimal user interaction, needing only a small number of labeled appliance activations. The method achieves competitive results compared to state-of-the-art methods, providing a practical and effective NILM solution suitable for widespread adoption by consumers and utility companies.

@Article{PMM25,
  author       = {Pusceddu, G. and Manca, S. and Massidda, L.},
  title        = {Fine-Tuning Non-Intrusive Load Monitoring Model Through User Interaction: A Practical Approach to Appliance Recognition with Limited Labeled Data},
  journal      = {Applied Energy},
  year         = {2025},
  publisher    = {Elsevier},
  keywords     = {visual and data-intensive computing, energy},
  url          = {https://publications.crs4.it/pubdocs/2025/PMM25},
}