Fine-tuning non-intrusive load monitoring model through user interaction: A practical approach to appliance recognition with limited labeled data

Gabriella Pusceddu, Simone Manca and Luca Massidda

Applied Energy, 2025, vol. 391, issue C, No S0306261925006737

Abstract: 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, requiring 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.

Keywords: Non-intrusive load monitoring; NILM; Energy disaggregation; Transfer learning; Deep learning; Smart meters; User interaction; Low-frequency data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125943

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