  Rapid renovation of Europe's inefficient buildings is required to reduce
climate change. However, analyzing and evaluating buildings at scale is
challenging because every building is unique. In current practice, the energy
performance of buildings is assessed during on-site visits, which are slow,
costly, and local. This paper presents a building point cloud dataset that
promotes a data-driven, large-scale understanding of the 3D representation of
buildings and their energy characteristics. We generate building point clouds
by intersecting building footprints with geo-referenced LiDAR data and link
them with attributes from UK's energy performance database via the Unique
Property Reference Number (UPRN). To achieve a representative sample, we select
one million buildings from a range of rural and urban regions across England,
of which half a million are linked to energy characteristics. Building point
clouds in new regions can be generated with the open-source code published
alongside the paper. The dataset enables novel research in building energy
modeling and can be easily expanded to other research fields by adding building
features via the UPRN or geo-location.
