Propagating Uncertainty in Solar Panel Performance for Life Cycle Modeling in Early Stage Design
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Yang_Propagating Uncertainty.pdf
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Author(s)
Honda, Tomonori
Chen, Heidi Qianyi
Chan, Kennis Y.
Yang, Maria
Date Issued
March 2011
Journal
Papers of the 2011 Spring Symposium of the Association for the Advancement of Artificial Intelligence, Artificial Intelligence and Sustainable Design
Publisher
Association for the Advancement of Artificial Intelligence Press
Citation
Honda, Tomonori et al. "Life Cycle Modeling in Early Stage Design." in Artificial Intelligence and Sustainable Design — Papers from the AAAI 2011 Spring Symposium (SS-11-02)
Version
Author's final manuscript
Abstract
One of the challenges in accurately applying metrics for life
cycle assessment lies in accounting for both irreducible and
inherent uncertainties in how a design will perform under
real world conditions. This paper presents a preliminary
study that compares two strategies, one simulation-based
and one set-based, for propagating uncertainty in a system.
These strategies for uncertainty propagation are then
aggregated. This work is conducted in the context of an
amorphous photovoltaic (PV) panel, using data gathered
from the National Solar Radiation Database, as well as
realistic data collected from an experimental hardware setup
specifically for this study. Results show that the influence of
various sources of uncertainty can vary widely, and in
particular that solar radiation intensity is a more significant
source of uncertainty than the efficiency of a PV panel. This
work also shows both set-based and simulation-based
approaches have limitations and must be applied
thoughtfully to prevent unrealistic results. Finally, it was
found that aggregation of the two uncertainty propagation
methods provided faster results than either method alone.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Engineering Systems Division
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
http://www.aaai.org/ocs/index.php/SSS/SSS11/paper/view/2477/2927
