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Title: Calibrating Building Energy Models Using Supercomputer Trained Machine Learning Agents

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.

Authors:
 [1];  [1];  [1];  [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States. Building Technologies Research and Integration Center (BTRIC). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC); USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1127381
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
Concurrency and Computation. Practice and Experience
Additional Journal Information:
Journal Name: Concurrency and Computation. Practice and Experience; Journal ID: ISSN 1532-0626
Publisher:
Wiley
Country of Publication:
United States
Language:
English

Citation Formats

Sanyal, Jibonananda, New, Joshua Ryan, Edwards, Richard, and Parker, Lynne Edwards. Calibrating Building Energy Models Using Supercomputer Trained Machine Learning Agents. United States: N. p., 2014. Web. doi:10.1002/cpe.3267.
Sanyal, Jibonananda, New, Joshua Ryan, Edwards, Richard, & Parker, Lynne Edwards. Calibrating Building Energy Models Using Supercomputer Trained Machine Learning Agents. United States. doi:10.1002/cpe.3267.
Sanyal, Jibonananda, New, Joshua Ryan, Edwards, Richard, and Parker, Lynne Edwards. Wed . "Calibrating Building Energy Models Using Supercomputer Trained Machine Learning Agents". United States. doi:10.1002/cpe.3267.
@article{osti_1127381,
title = {Calibrating Building Energy Models Using Supercomputer Trained Machine Learning Agents},
author = {Sanyal, Jibonananda and New, Joshua Ryan and Edwards, Richard and Parker, Lynne Edwards},
abstractNote = {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.},
doi = {10.1002/cpe.3267},
journal = {Concurrency and Computation. Practice and Experience},
issn = {1532-0626},
number = ,
volume = ,
place = {United States},
year = {2014},
month = {1}
}

Works referenced in this record:

Supercomputer assisted generation of machine learning agents for the calibration of building energy models
conference, January 2013

  • Sanyal, Jibonananda; New, Joshua; Edwards, Richard
  • Proceedings of the Conference on Extreme Science and Engineering Discovery Environment Gateway to Discovery - XSEDE '13
  • DOI: 10.1145/2484762.2484818