ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series
Summary: ADF: subsequence-based framework that converts long, variable, low-frequency smart‑meter consumption series into manageable inputs for appliance presence/absence detection. TransApp: a Transformer time‑series classifier with self‑supervised pretraining that outperforms prior SOTA on two large real datasets. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Adrien Petralia (Université Paris Cité; Électricité de France)
- 2. Philippe Charpentier (Électricité de France)
- 3. Themis Palpanas (Institut Universitaire de France; Université Paris Cité)
BibTeX Citation
@article{petralia_vldb24,
title = {{ADF \& TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series}},
author = {Petralia, Adrien and Charpentier, Philippe and Palpanas, Themis},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {3},
pages = {553--562},
doi = {10.14778/3632093.3632115},
url = {https://doi.org/10.14778/3632093.3632115},
year = {2024}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,987 | Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series | 2023 | VLDB | 6.9722766e-05 |
| 9,394 | iEDeaL: A Deep Learning Framework for Detecting Highly Imbalanced Interictal Epileptiform Discharges | 2023 | VLDB | 5.2755515e-05 |
| 9,479 | dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification | 2022 | SIGMOD | 5.2634238e-05 |
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