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Title: Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

Abstract

Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions were examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and combinations of predictive variables were examined. A penalized logistic regression model which was fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at various time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. In conclusion, the methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.

Authors:
 [1];  [1];  [1];  [1];  [1];  [2];  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Smarter Decisions, LLC, Portland, OR (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1395358
Report Number(s):
PNNL-SA-126567
Journal ID: ISSN 0306-2619; PII: S0306261917313697
Grant/Contract Number:
AC05-76RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 205; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Electrical grid; Grid stress; Heatwave; Statistical modeling

Citation Formats

Bramer, Lisa M., Rounds, J., Burleyson, C. D., Fortin, D., Hathaway, J., Rice, J., and Kraucunas, I.. Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days. United States: N. p., 2017. Web. doi:10.1016/J.APENERGY.2017.09.087.
Bramer, Lisa M., Rounds, J., Burleyson, C. D., Fortin, D., Hathaway, J., Rice, J., & Kraucunas, I.. Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days. United States. doi:10.1016/J.APENERGY.2017.09.087.
Bramer, Lisa M., Rounds, J., Burleyson, C. D., Fortin, D., Hathaway, J., Rice, J., and Kraucunas, I.. Fri . "Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days". United States. doi:10.1016/J.APENERGY.2017.09.087.
@article{osti_1395358,
title = {Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days},
author = {Bramer, Lisa M. and Rounds, J. and Burleyson, C. D. and Fortin, D. and Hathaway, J. and Rice, J. and Kraucunas, I.},
abstractNote = {Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions were examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and combinations of predictive variables were examined. A penalized logistic regression model which was fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at various time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. In conclusion, the methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.},
doi = {10.1016/J.APENERGY.2017.09.087},
journal = {Applied Energy},
number = ,
volume = 205,
place = {United States},
year = {Fri Sep 22 00:00:00 EDT 2017},
month = {Fri Sep 22 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on September 22, 2018
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