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

Journal Article · · Concurrency and Computation. Practice and Experience
DOI:https://doi.org/10.1002/cpe.3267· OSTI ID:1127381
 [1];  [1];  [1];  [1]
  1. ORNL
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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.

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

References (5)

U.S. Department of Energy Commercial Reference Building Models of the National Building Stock report February 2011
Supercomputer assisted generation of machine learning agents for the calibration of building energy models
  • Sanyal, Jibonananda; New, Joshua; Edwards, Richard
  • Proceedings of the Conference on Extreme Science and Engineering Discovery Environment Gateway to Discovery - XSEDE '13 https://doi.org/10.1145/2484762.2484818
conference January 2013
Approximate l-Fold Cross-Validation with Least Squares SVM and Kernel Ridge Regression conference December 2013
Estimating building simulation parameters via Bayesian structure learning
  • Edwards, Richard E.; New, Joshua R.; Parker, Lynne E.
  • Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining Algorithms, Systems, Programming Models and Applications - BigMine '13 https://doi.org/10.1145/2501221.2501226
conference January 2013
Predicting future hourly residential electrical consumption: A machine learning case study journal June 2012

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