Enabling immersive engagement in energy system models with deep learning

Bugbee, Bruce; Bush, Brian W.; Gruchalla, Kenny; Potter, Kristin; Brunhart‐Lupo, Nicholas; Krishnan, Venkat

doi:10.1002/sam.11419

Title: Enabling immersive engagement in energy system models with deep learning

Journal Article · Thu Jun 13 00:00:00 EDT 2019 · Statistical Analysis and Data Mining

DOI:https://doi.org/10.1002/sam.11419· OSTI ID:1543128

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^[1]; Potter, Kristin ^[1]; Brunhart‐Lupo, Nicholas ^[1];

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National Renewable Energy Lab. (NREL), Golden, CO (United States)

Complex ensembles of energy simulation models have become crucial components of renewable energy research in recent years. Often the increasing 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 determined 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.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1543128

Report Number(s):: NREL/JA-6A20-73878; MainId:12336; UUID:0f2a80ee-a96e-e911-9c21-ac162d87dfe5; MainAdminID:816

Journal Information:: Statistical Analysis and Data Mining, Vol. 12, Issue 4; ISSN 1932-1864

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 4 works

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