Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models: Preprint

Kristin Potter, Nicholas Brunhart-Lupo, Brian Bush, Kenny Gruchalla, Venkat Krishnan, Bruce Bugbee

Research output: Contribution to conferencePaper


We have developed a framework for the exploration, design, and planning of energy systems that combines interactive visualization with machine-learning based approximations of simulations through a general purpose dataflow API. Our system provides a visual inter- face allowing users to explore an ensemble of energy simulations representing a subset of the complex input parameter space, and spawn new simulations to 'fill in' input regions corresponding to new enegery system scenarios. Unfortunately, many energy simula- tions are far too slow to provide interactive responses. To support interactive feedback, we are developing reduced-form models via machine learning techniques, which provide statistically sound esti- mates of the full simulations at a fraction of the computational cost and which are used as proxies for the full-form models. Fast com- putation and an agile dataflow enhance the engagement with energy simulations, and allow researchers to better allocate computational resources to capture informative relationships within the system and provide a low-cost method for validating and quality-checking large-scale modeling efforts.
Original languageAmerican English
Number of pages7
StatePublished - 2017
EventIEEE Data Systems for Interactive Analysis - Phoenix, Arizona
Duration: 2 Oct 20176 Oct 2017


ConferenceIEEE Data Systems for Interactive Analysis
CityPhoenix, Arizona

Bibliographical note

See NREL/CP-2C00-72246 for paper as published in IEEE proceedings

NREL Publication Number

  • NREL/CP-2C00-70112


  • artificial neural networks
  • energy system simulation
  • immersive visualization
  • machine learning


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