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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

In recent years, smart meters have been widely adopted by electricity suppliers to improve the management of the smart grid system. These meters usually collect energy consumption data at a very low frequency (every 30min), enabling utilities to bill customers more accurately. To provide more personalized recommendations, the next step is to detect the appliances owned by customers, which is a challenging problem, due to the very-low meter reading frequency. Even though the appliance detection problem can be cast as a time series classification problem, with many such classifiers having been proposed in the literature, no study has applied and compared them on this specific problem. This paper presents an in-depth evaluation and comparison of state-of-the-art time series classifiers applied to detecting the presence/absence of diverse appliances in very low-frequency smart meter data. We report results with five real datasets. We first study the impact of the detection quality of 13 different appliances using 30min sampled data, and we subsequently propose an analysis of the possible detection performance gain by using a higher meter reading frequency. The results indicate that the performance of current time series classifiers varies significantly. Some of them, namely deep learning-based classifiers, provide promising results in terms of accuracy (especially for certain appliances), even using 30min sampled data, and are scalable to the large smart meter time series collections of energy consumption data currently available to electricity suppliers. Nevertheless, our study shows that more work is needed in this area to further improve the accuracy of the proposed solutions.

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

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015