  Building Information Modeling has been used to analyze as well as increase
the energy efficiency of the buildings. It has shown significant promise in
existing buildings by deconstruction and retrofitting. Current cities which
were built without the knowledge of energy savings are now demanding better
ways to become smart in energy utilization. However, the existing methods of
generating BIMs work on building basis. Hence they are slow and expensive when
we scale to a larger community or even entire towns or cities. In this paper,
we propose a method to creation of prototype buildings that enable us to match
and generate statistics very efficiently. Our method suggests better energy
efficient prototypes for the existing buildings. The existing buildings are
identified and located in the 3D point cloud. We perform experiments on
synthetic dataset to demonstrate the working of our approach.

Machine Learning