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Building Energy Estimation using Machine Learning: Rennes use case

Research datakeyboard_double_arrow_right Dataset 02 Jul 2025Publisher:Zenodo

Authors: BELAID, Mohamed-Bachir; Ebrahimi, Babak;

doi: 10.5281/zenodo.15789520 , 10.5281/zenodo.15789521

Building Energy Estimation using Machine Learning: Rennes use case

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Abstract

This CSV file contains building-level energy demand estimations for the city of Rennes, computed using the XGboost, a tree based Machine Learning approach. The model uses buildings "number_of_levels", "area_of_heat_loss_opaque_vertical_walls", "year_of_construction" as input features. This dataset is an output of FAIRiCUBE Use Case 4 (UC4): Spatial and temporal assessment of neighbourhood building stock, which aims to evaluate energy consumption and material stocks in urban environments using harmonized methods across European cities.

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