Building Energy Estimation using Machine Learning: Rennes use case

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.