Enabling Immersive Engagement in Energy System Models with Deep Learning

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

Research output: Contribution to journalArticlepeer-review

7 Scopus Citations

Abstract

Complex ensembles of energy simulation models have become significant components of renewable energy research in recent years. Often the significant computational cost, high-dimensional structure, and other complexities hinder researchers from fully utilizing these data sources for knowledge building. Researchers at National Renewable Energy Laboratory have developed an immersive visualization workflow to dramatically improve user engagement and analysis capability through a combination of low-dimensional structure analysis, deep learning, and custom visualization methods. We present case studies for two energy simulation platforms.

Original languageAmerican English
Pages (from-to)325-337
Number of pages13
JournalStatistical Analysis and Data Mining
Volume12
Issue number4
DOIs
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 The Authors. Statistical Analysis and Data Mining: The ASA Data Science Journal published by Wiley Periodicals, Inc.

NREL Publication Number

  • NREL/JA-6A20-73878

Keywords

  • high-dimensional data
  • interactive visualization
  • neural networks
  • renewable energy
  • t-SNE
  • Tucker decomposition

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