Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series

2025-06-06

AI Summary

To address the scarcity of fine-grained appliance-level labels in non-intrusive load monitoring (NILM), this paper proposes CamAL, a weakly supervised appliance pattern localization framework that requires only household-level appliance presence labels—eliminating the need for time-stamped, instance-level annotations. CamAL integrates deep classifier ensembles, gradient-weighted class activation mapping (Grad-CAM), and weakly supervised temporal modeling to achieve interpretable, precise localization of appliance operational periods. Evaluated on four real-world datasets, CamAL significantly outperforms existing weakly supervised NILM methods. Remarkably, it achieves performance comparable to state-of-the-art fully supervised approaches using only a minimal number of coarse labels, thereby drastically reducing annotation effort and cost. This work establishes a highly efficient and practical paradigm for NILM under severe label scarcity, advancing both interpretability and scalability in real-world energy disaggregation applications.

Technology Category

• Machine Learning: Active Learning
• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Improving smart grid system management is crucial in the fight against climate change, and enabling consumers to play an active role in this effort is a significant challenge for electricity suppliers. In this regard, millions of smart meters have been deployed worldwide in the last decade, recording the main electricity power consumed in individual households. This data produces valuable information that can help them reduce their electricity footprint; nevertheless, the collected signal aggregates the consumption of the different appliances running simultaneously in the house, making it difficult to apprehend. Non-Intrusive Load Monitoring (NILM) refers to the challenge of estimating the power consumption, pattern, or on/off state activation of individual appliances using the main smart meter signal. Recent methods proposed to tackle this task are based on a fully supervised deep-learning approach that requires both the aggregate signal and the ground truth of individual appliance power. However, such labels are expensive to collect and extremely scarce in practice, as they require conducting intrusive surveys in households to monitor each appliance. In this paper, we introduce CamAL, a weakly supervised approach for appliance pattern localization that only requires information on the presence of an appliance in a household to be trained. CamAL merges an ensemble of deep-learning classifiers combined with an explainable classification method to be able to localize appliance patterns. Our experimental evaluation, conducted on 4 real-world datasets, demonstrates that CamAL significantly outperforms existing weakly supervised baselines and that current SotA fully supervised NILM approaches require significantly more labels to reach CamAL performances. The source of our experiments is available at: https://github.com/adrienpetralia/CamAL. This paper appeared in ICDE 2025.

Problem

• Localizing appliance patterns using weakly supervised learning
• Reducing label dependency in smart meter data analysis
• Improving Non-Intrusive Load Monitoring with fewer labels

Innovation

• Weakly supervised appliance pattern localization
• Ensemble of deep-learning classifiers
• Explainable classification method