The « PEPER » project will extend its data collection architecture model in Saclay

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The « PEPER » project will extend its data collection architecture model in Saclay

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The « PEPER » project: an architecture for collecting data on energy consumption per second at Télécom SudParis.
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Selected as part of the DATAIA Institute's « Calls for Research Projects » in 2018, the « PEPER » project aims to use neural networks for prediction and reinforcement in the energy management optimization.


« Thanks to the funds provided by DATAIA, we were able to cable a student room building at Télécom SudParis to record energy consumption to an accuracy of the second »


explains Hossam Afifi, professeur at Télécom SudParis and leader of this project with Florence Ossart, professor at the Laboratoire de Génie Electrique de Paris (CNRS, CentraleSupélec, Université Paris-Sud, Sorbonne Université), and Jordi Badosa,project manager at the École Polytechnique in the Laboratory of Dynamic Meteorology (CNRS, École Polytechnique, ENS Paris-Saclay, Sorbonne Université).

Two groups of students and interns have worked together to implement a Cloud architecture. Thus the data is stored in a special database (InfluxDB).

A Web application (Bokeh) allows the visualization of time series. Python applications using the TensorFlow library allow to make predictions based on dense LSTM and GRU networks.

A classification of building types and their consumption is done by classical convolutional networks.

All this has been made available to the public on a Github. Many publications have described the architecture and results.

Another deployment is planned in one of the public places of the Paris-Saclay town halls.

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The « PEPER » project

The world of electrical energy is facing significant structural changes: electricity use is constantly increasing and climate challenges require an increase in the share of renewable energies in production (solar and wind). 

The objective of the PEPER project is to collect data on the different actors of this network, and to use learning and Deep Reinforcement Learning techniques to develop algorithms to predict the production and consumption of each actor, and the cooperation between them.


PEPER team

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Université Paris-Saclay