Abstract
Building Energy Modeling (BEM) is an approach to model
the energy usage in buildings for design and retrofit purposes.
EnergyPlus is the flagship Department of Energy
software that performs BEM for different types of buildings.
The input to EnergyPlus can often extend in the order of a
few thousand parameters which have to be calibrated manually
by an expert for realistic energy modeling. This makes
it challenging and expensive thereby making building energy
modeling unfeasible for smaller projects. In this paper,
we describe the “Autotune” research which employs machine
learning algorithms to generate agents for the different kinds
of standard reference buildings in the U.S. building stock.
The parametric space and the variety of building locations
and types make this a challenging computational problem
necessitating the use of supercomputers. Millions of EnergyPlus
simulations are run on supercomputers which are
subsequently used to train machine learning algorithms to
generate agents. These agents, once created, can then run
in a fraction of the time thereby allowing cost-effective calibration
of building models.
