Supercomputer Assisted Generation of Machine Learning Agents for the Calibration of Building Energy Models
- ORNL
Building Energy Modeling (BEM) is an approach to model the energy usage in buildings for design and retrot pur- poses. EnergyPlus is the agship Department of Energy software that performs BEM for dierent types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters which have to be calibrated manu- ally by an expert for realistic energy modeling. This makes it challenging and expensive thereby making building en- ergy modeling unfeasible for smaller projects. In this paper, we describe the \Autotune" research which employs machine learning algorithms to generate agents for the dierent 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 En- ergyPlus 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-eective cali- bration of building models.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Building Technologies Research and Integration Center (BTRIC); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Center for Computational Sciences (NCCS)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 1087484
- Resource Relation:
- Conference: XSEDE13, San Diego, CA, USA, 20130722, 20130725
- Country of Publication:
- United States
- Language:
- English
Similar Records
Calibrating Building Energy Models Using Supercomputer Trained Machine Learning Agents
Autotune E+ Building Energy Models