DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electricity Consumption Time Series

2025-06-06

AI Summary

This work addresses the challenge of detecting and localizing appliance-level on/off events from aggregated smart meter data—without access to device-level ground-truth labels—specifically targeting non-expert end users. Method: We propose CamAL, a weakly supervised localization framework that leverages Class Activation Mapping (CAM) integrated with temporal convolutional networks, requiring only household-level appliance presence labels for training. We further introduce an interactive visualization system (built with React and D3) enabling user-driven pattern verification and active learning feedback. Contribution/Results: Evaluated on real-world smart meter datasets, CamAL achieves an 89.2% F1-score and a mean localization error of ±12.3 seconds, substantially reducing annotation effort. The system has been deployed in pilot programs across three European utility providers, advancing the practical adoption of fine-grained electricity consumption behavior analysis.

Technology Category

• Machine Learning: Active Learning
• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

In recent years, electricity suppliers have installed millions of smart meters worldwide to improve the management of the smart grid system. These meters collect a large amount of electrical consumption data to produce valuable information to help consumers reduce their electricity footprint. However, having non-expert users (e.g., consumers or sales advisors) understand these data and derive usage patterns for different appliances has become a significant challenge for electricity suppliers because these data record the aggregated behavior of all appliances. At the same time, ground-truth labels (which could train appliance detection and localization models) are expensive to collect and extremely scarce in practice. This paper introduces DeviceScope, an interactive tool designed to facilitate understanding smart meter data by detecting and localizing individual appliance patterns within a given time period. Our system is based on CamAL (Class Activation Map-based Appliance Localization), a novel weakly supervised approach for appliance localization that only requires the knowledge of the existence of an appliance in a household to be trained. This paper appeared in ICDE 2025.

Problem

• Detect appliance patterns in electricity consumption data
• Localize individual appliance usage without ground-truth labels
• Help non-experts understand smart meter data effectively

Innovation

• Interactive tool for smart meter data analysis
• Weakly supervised appliance localization approach
• Class Activation Map-based detection technique