Adrien Petralia

Adrien Petralia

Third-year Computer Science Ph.D. candidate

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

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portfolio

publications

Pumping station design based on shape optimization process

Published in TELEMAC-MASCARET User Conference, 2020

Optimization applications with TELEMAC are increasing due to interoperability development of the system module. The present work is based on a shape optimization process apply to a real problem: the optimization of the streamline trajectories in front of a pumping station intakes. Deflectors have been designed in the model upstream of the intakes to drive the flow as perpendicular as possible to the intake entrances. The deflector’s shape is defined based on two parameters controlling the size and the orientation respectively. In a first step, a cost function evaluating the orientation of the streamlines was defined. Then, a study was carried out on these two parameters to estimate, for each deflector, which configuration minimizes the cost function based on TELEMAC-2D runs. Finally, a statistical emulator was used to link the input parameters with the cost function residual. Indeed, this metamodeling technique allowed a simplification of the TELEMAC-2D study, drastically reducing computational times. This was particularly useful to apply an optimization process on the parameters of the shapes, requiring many TELEMAC-2D study runs.

Recommended citation: Florent Taccone, Cédric Goeury, Fabrice Zaoui, and Adrien Petralia. (2020). "Pumping station design based on shape optimization process." Proceedings of the 27th TELEMAC-MASCARET User Conference.

Appliance Detection Using Very Low-Frequency Smart Meter Time Series

Published in e-Energy '23: Proceedings of the 14th ACM International Conference on Future Energy Systems, 2023

Full text access here

Recommended citation: Adrien Petralia, Philippe Charpentier, Paul Boniol, and Themis Palpanas. 2023. Appliance Detection Using Very Low-Frequency Smart Meter Time Series. In The 14th ACM International Conference on Future Energy Systems (e-Energy ’23), June 20–23, 2023, Orlando, FL, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3575813.359519. https://doi.org/10.1145/3575813.3595198

ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series

Published in Proceedings of the VLDB Endowment, 2023

Over the past decade, millions of smart meters have been installed by electricity suppliers worldwide, allowing them to collect a large amount of electricity consumption data, albeit sampled at a low frequency (one point every 30min). One of the important challenges these suppliers face is how to utilize these data to detect the presence/absence of different appliances in the customers’ households. This valuable information can help them provide personalized offers and recommendations to help customers towards the energy transition. Appliance detection can be cast as a time series classification problem. However, the large amount of data combined with the long and variable length of the consumption series pose challenges when training a classifier. In this paper, we propose ADF, a framework that uses subsequences of a client consumption series to detect the presence/absence of appliances. We also introduce TransApp, a Transformer-based time series classifier that is first pretrained in a self-supervised way to enhance its performance on appliance detection tasks. We test our approach on two real datasets, including a publicly available one. The experimental results with two large real datasets show that the proposed approach outperforms current solutions, including state-of-the-art time series classifiers applied to appliance detection.

Recommended citation: Adrien Petralia, Philippe Charpentier, and Themis Palpanas. ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series. PVLDB, 17(3): 553-562, 2023. doi:10.14778/3632093.363211 https://doi.org/10.14778/3632093.3632115

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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