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Title: Fast Learning for Immersive Engagement in Energy Simulations

Abstract

The fast computation which is critical for immersive engagement with and learning from energy simulations would be furthered by developing a general method for creating rapidly computed simplified versions of NREL's computation-intensive energy simulations. Created using machine learning techniques, these 'reduced form' simulations can provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost with response times - typically less than one minute of wall-clock time - suitable for real-time human-in-the-loop design and analysis. Additionally, uncertainty quantification techniques can document the accuracy of the approximate models and their domain of validity. Approximation methods are applicable to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. These reduced-form representations cannot replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and quality assurance for large sets of simulations. We present an overview of the framework and methods we have implemented for developing these reduced-form representations.

Authors:
ORCiD logo [1];  [1];  [1]; ORCiD logo [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1435498
Report Number(s):
NREL/PO-6A20-70089
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the NREL 2017 LDRD Poster Session, 1 June 2017, Golden, Colorado
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; 29 ENERGY PLANNING, POLICY, AND ECONOMY; machine learning; deep learning; energy system modeling; simulation; visualization; reduced-form model; metamodel

Citation Formats

Bush, Brian W, Bugbee, Bruce, Gruchalla, Kenny M, Krishnan, Venkat K, and Potter, Kristin C. Fast Learning for Immersive Engagement in Energy Simulations. United States: N. p., 2018. Web.
Bush, Brian W, Bugbee, Bruce, Gruchalla, Kenny M, Krishnan, Venkat K, & Potter, Kristin C. Fast Learning for Immersive Engagement in Energy Simulations. United States.
Bush, Brian W, Bugbee, Bruce, Gruchalla, Kenny M, Krishnan, Venkat K, and Potter, Kristin C. Wed . "Fast Learning for Immersive Engagement in Energy Simulations". United States. doi:. https://www.osti.gov/servlets/purl/1435498.
@article{osti_1435498,
title = {Fast Learning for Immersive Engagement in Energy Simulations},
author = {Bush, Brian W and Bugbee, Bruce and Gruchalla, Kenny M and Krishnan, Venkat K and Potter, Kristin C},
abstractNote = {The fast computation which is critical for immersive engagement with and learning from energy simulations would be furthered by developing a general method for creating rapidly computed simplified versions of NREL's computation-intensive energy simulations. Created using machine learning techniques, these 'reduced form' simulations can provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost with response times - typically less than one minute of wall-clock time - suitable for real-time human-in-the-loop design and analysis. Additionally, uncertainty quantification techniques can document the accuracy of the approximate models and their domain of validity. Approximation methods are applicable to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. These reduced-form representations cannot replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and quality assurance for large sets of simulations. We present an overview of the framework and methods we have implemented for developing these reduced-form representations.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Apr 25 00:00:00 EDT 2018},
month = {Wed Apr 25 00:00:00 EDT 2018}
}

Conference:
Other availability
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