Enabling immersive engagement in energy system models with deep learning. (13th June 2019)

• Record Type: Journal Article
• Authors: Bugbee, Bruce; Bush, Brian W.; Gruchalla, Kenny; Potter, Kristin; Brunhart‐Lupo, Nicholas; Krishnan, Venkat
• Other Names: Lawrence Earl guestEditor.
• Is Part Of: Statistical analysis and data mining. Volume 12:Number 4(2019)
• Page Start: 325
• Page End: 337
• Publication Date: 2019-06-13
• Subjects: high‐dimensional data -- interactive visualization -- neural networks -- renewable energy -- t‐SNE -- Tucker decomposition; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11419
• Languages: English
• ISSNs: 1932-1864

Abstract

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.