Building Energy Estimation using Machine Learning: Rennes use case
收藏Zenodo2025-07-27 更新2026-05-26 收录
下载链接：
https://zenodo.org/doi/10.5281/zenodo.15789520
下载链接
链接失效反馈官方服务：
资源简介：
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.
提供机构：
Zenodo
创建时间：
2025-07-27
