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

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

Research output: Contribution to conferencePaperpeer-review

4 Scopus Citations

Abstract

We describe a new framework that allows users to explore and steer ensembles of energy systems simulations by coupling multiple energy models and interactive visualization through a dataflow API. Through the visual interface, users can interactively explore complex parameter spaces populated by hundreds, or thousands, of simulation runs and interactively spawn new simulations to 'fill in' regions of interest in the parameter space. The computational and visualization capabilities reside within a general-purpose dataflow architecture for connecting producers of multidimensional timeseries data, such as energy simulations, with consumers of that data, whether they be visualizations, statistical analyses, or datastores. Fast computation and agile dataflow can enhance the engagement with energy simulations, allowing users to populate the parameter space in real time. However, many energy simulations are far too slow to provide an interactive response. To support interactive feedback, we are creating reduced-form simulations developed through machine learning techniques, which provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost. These reduced-form simulations have response times on the order of seconds, suitable for real-time human-in-the-loop design and analysis. The approximation methods apply to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. Such reduced-form representations do not replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and exploration for large ensembles of simulations. The improved understanding, facilitated by the reduced-form models, dataflow API, and visualization tools, allows researchers to better allocate computational resources to capture informative relationships within the system as well as provide a low-cost method for validating and quality-checking large-scale modeling efforts.

Original languageAmerican English
Pages1-5
Number of pages5
DOIs
StatePublished - 2 Jul 2017
Event2017 IEEE Workshop on Data Systems for Interactive Analysis, DSIA 2017 - Phoenix, United States
Duration: 1 Oct 20172 Oct 2017

Conference

Conference2017 IEEE Workshop on Data Systems for Interactive Analysis, DSIA 2017
Country/TerritoryUnited States
CityPhoenix
Period1/10/172/10/17

Bibliographical note

See NREL/CP-2C00-70112 for preprint

NREL Publication Number

  • NREL/CP-2C00-72246

Keywords

  • databases
  • Deep learning
  • energy system models
  • ensemble visualization
  • immersive visualization
  • multidimensional time-series
  • neural networks

Fingerprint

Dive into the research topics of 'Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models'. Together they form a unique fingerprint.

Cite this