Fast Learning for Immersive Engagement in Energy Simulations

Brian Bush, Kenny Gruchalla, Venkat Krishnan, Kristin Potter, Bruce Bugbee

Research output: NRELPoster


The fast computation which is critical for immersive engagement with and learning from energy simulations would be furthered by developing a general method for creating rapidly computed simplified versions of NREL's computation-intensive energy simulations. Created using machine learning techniques, these 'reduced form' simulations can provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost with response times - typically less than one minute of wall-clock time - suitable for real-time human-in-the-loop design and analysis. Additionally, uncertainty quantification techniques can document the accuracy of the approximate models and their domain of validity. Approximation methods are applicable to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. These reduced-form representations cannot replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and quality assurance for large sets of simulations. We present an overview of the framework and methods we have implemented for developing these reduced-form representations.
Original languageAmerican English
StatePublished - 2017

Publication series

NamePresented at the NREL 2017 LDRD Poster Session, 1 June 2017, Golden, Colorado

NREL Publication Number

  • NREL/PO-6A20-70089


  • deep learning
  • energy system modeling
  • machine learning
  • metamodel
  • reduced-form model
  • simulation
  • visualization


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