Smoothing local energy consumption



Make the most of renewable energy for a pool of buildings with multiple usage


Build models to predict local green energy consumption in order to make investment plan at the scale of a district.








The tool makes it possible to distribute the value generated by the solar energy between the occupants of the district according to their consumption profile and thus to propose to these actors a project of investment and equipment profitable and dimensioned for their needs.


The experimentation took place in Paris.



The experimentation took place in an eco-district with solar power production. Partners are providing the following data sets: characteristics of a sample of the estates of BNPP REPM, heating and cooling consumption data The selected startup – BeeBryte - offers a SaaS platform using AI to help commercial and industrial buildings reduce both their energy bill and their carbon footprint, through a smarter monitoring of their electrical consumption.






Each profession, every user has a different way of consuming energy. All use energy differently but their billing methods are the same. By default, distribution rules per square meter are applied, which is not optimal.





The adoption of solar electricity faces strong barriers, one being the investment required for a solar installation. This project aims at popularizing the adoption of solar production by highlighting the benefits and efficiency of solar installations depending on the area of implementation.




Please reload

A program designed by NUMA in partnership with the City of Paris