Enabling immersive engagement in energy system models with deep learning
Abstract
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.
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- OSTI Identifier:
- 1543128
- Report Number(s):
- NREL/JA-6A20-73878
Journal ID: ISSN 1932-1864; MainId:12336;UUID:0f2a80ee-a96e-e911-9c21-ac162d87dfe5;MainAdminID:816
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Statistical Analysis and Data Mining
- Additional Journal Information:
- Journal Volume: 12; Journal Issue: 4; Journal ID: ISSN 1932-1864
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; high-dimensional data; interactive visualization; neural networks; renewable energy; t-SNE; Tucker decomposition
Citation Formats
Bugbee, Bruce, Bush, Brian W., Gruchalla, Kenny, Potter, Kristin, Brunhart‐Lupo, Nicholas, and Krishnan, Venkat. Enabling immersive engagement in energy system models with deep learning. United States: N. p., 2019.
Web. doi:10.1002/sam.11419.
Bugbee, Bruce, Bush, Brian W., Gruchalla, Kenny, Potter, Kristin, Brunhart‐Lupo, Nicholas, & Krishnan, Venkat. Enabling immersive engagement in energy system models with deep learning. United States. https://doi.org/10.1002/sam.11419
Bugbee, Bruce, Bush, Brian W., Gruchalla, Kenny, Potter, Kristin, Brunhart‐Lupo, Nicholas, and Krishnan, Venkat. Thu .
"Enabling immersive engagement in energy system models with deep learning". United States. https://doi.org/10.1002/sam.11419. https://www.osti.gov/servlets/purl/1543128.
@article{osti_1543128,
title = {Enabling immersive engagement in energy system models with deep learning},
author = {Bugbee, Bruce and Bush, Brian W. and Gruchalla, Kenny and Potter, Kristin and Brunhart‐Lupo, Nicholas and Krishnan, Venkat},
abstractNote = {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.},
doi = {10.1002/sam.11419},
journal = {Statistical Analysis and Data Mining},
number = 4,
volume = 12,
place = {United States},
year = {Thu Jun 13 00:00:00 EDT 2019},
month = {Thu Jun 13 00:00:00 EDT 2019}
}
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Works referenced in this record:
Visualizing the Positional and Geometrical Variability of Isosurfaces in Uncertain Scalar Fields
journal, June 2011
- Pfaffelmoser, Tobias; Reitinger, Matthias; Westermann, Rüdiger
- Computer Graphics Forum, Vol. 30, Issue 3
Interactions of rooftop PV deployment with the capacity expansion of the bulk power system
journal, April 2016
- Cole, Wesley; Lewis, Haley; Sigrin, Ben
- Applied Energy, Vol. 168
Deep learning
journal, May 2015
- LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
- Nature, Vol. 521, Issue 7553
Wind Vision: A New Era for Wind Power in the United States
journal, November 2015
- Wiser, Ryan; Lantz, Eric; Mai, Trieu
- The Electricity Journal, Vol. 28, Issue 9
Long Short-Term Memory
journal, November 1997
- Hochreiter, Sepp; Schmidhuber, Jürgen
- Neural Computation, Vol. 9, Issue 8
Simulation exploration through immersive parallel planes
conference, March 2016
- Brunhart-Lupo, Nicholas; Bush, Brian W.; Gruchalla, Kenny
- 2016 Workshop on Immersive Analytics (IA)
Coupling visualization, simulation, and deep learning for ensemble steering of complex energy models
conference, October 2017
- Bush, Brian; Brunhart-Lupo, Nicholas; Bugbee, Bruce
- 2017 IEEE Workshop on Data Systems for Interactive Analysis (DSIA)
Deep Residual Learning for Image Recognition
conference, June 2016
- He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey
journal, March 2013
- Kehrer, Johannes; Hauser, Helwig
- IEEE Transactions on Visualization and Computer Graphics, Vol. 19, Issue 3
Tensor Decompositions and Applications
journal, August 2009
- Kolda, Tamara G.; Bader, Brett W.
- SIAM Review, Vol. 51, Issue 3
Evaluating the value of high spatial resolution in national capacity expansion models using ReEDS
conference, July 2016
- Krishnan, Venkat; Cole, Wesley
- 2016 IEEE Power and Energy Society General Meeting (PESGM)
Visual Analytics for Complex Engineering Systems: Hybrid Visual Steering of Simulation Ensembles
journal, December 2014
- Matkovic, Kresimir; Gracanin, Denis; Splechtna, Rainer
- IEEE Transactions on Visualization and Computer Graphics, Vol. 20, Issue 12
Future challenges for ensemble visualization
journal, May 2014
- Obermaier, Harald; Joy, Kenneth I.
- IEEE Computer Graphics and Applications, Vol. 34, Issue 3
Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data
conference, December 2009
- Potter, Kristin; Wilson, Andrew; Bremer, Peer-Timo
- 2009 IEEE International Conference on Data Mining Workshops (ICDMW)
Visual Analysis and Steering of Flooding Simulations
journal, June 2013
- Ribicic, H.; Waser, J.; Fuchs, R.
- IEEE Transactions on Visualization and Computer Graphics, Vol. 19, Issue 6
Noodles: A Tool for Visualization of Numerical Weather Model Ensemble Uncertainty
journal, November 2010
- Sanyal, J.; Dyer, J.
- IEEE Transactions on Visualization and Computer Graphics, Vol. 16, Issue 6
Visual Parameter Space Analysis: A Conceptual Framework
journal, December 2014
- Sedlmair, Michael; Heinzl, Christoph; Bruckner, Stefan
- IEEE Transactions on Visualization and Computer Graphics, Vol. 20, Issue 12
Nodes on Ropes: A Comprehensive Data and Control Flow for Steering Ensemble Simulations
journal, December 2011
- Waser, J.; Ribicic, H.; Fuchs, R.
- IEEE Transactions on Visualization and Computer Graphics, Vol. 17, Issue 12
Deep and Confident Prediction for Time Series at Uber
conference, November 2017
- Zhu, Lingxue; Laptev, Nikolay
- 2017 IEEE International Conference on Data Mining Workshops (ICDMW)
High-dimensional data visualization
journal, February 2020
- Tang, Lin
- Nature Methods, Vol. 17, Issue 2
National Economic Value Assessment of Plug-In Electric Vehicles Volume I
text, January 2016
- Melaina, Marc; Bush, Brian; Eichman, Joshua
- NREL