Each week, we select a recently published Open Access article to feature. This week’s article comes from Statistical Analysis and Data Mining and presents an immersive visualization workflow that enables immersive engagement in energy system models.

The article’s abstract is given below, with the full article available to read here.

Bugbee, B, Bush, BW, Gruchalla, K Potter, K, Brunhart‐Lupo, N, Krishnan, V. Enabling immersive engagement in energy system models with deep learning. Stat Anal Data Min: The ASA Data Sci Journal. 2019; 12: 325– 337. https://doi.org/10.1002/sam.11419

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.