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

arXiv: https://arxiv.org/abs/2506.05912 · HTML: https://mlsanity.com/paper/2506.05912/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification](https://mlsanity.com/paper/2207.12165/) (2022)
- [Appliance Detection Using Very Low-Frequency Smart Meter Time Series](https://mlsanity.com/paper/2305.10352/) (2023)
- [ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series](https://mlsanity.com/paper/2401.05381/) (2023)

## Built on by (most cited first, 1 of 1)

- [LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics](https://mlsanity.com/paper/2609.11639/) (2026)

## Abstract (arXiv)

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.