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

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  • Bramer, L.M.
  • Rounds, J.
  • Burleyson, C.D.
  • Fortin, D.
  • Hathaway, J.
  • Rice, J.
  • Kraucunas, I.

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. 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.

Suggested Citation

  • Bramer, L.M. & Rounds, J. & Burleyson, C.D. & Fortin, D. & Hathaway, J. & Rice, J. & Kraucunas, I., 2017. "Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days," Applied Energy, Elsevier, vol. 205(C), pages 1408-1418.
    • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:1408-1418
    DOI: 10.1016/j.apenergy.2017.09.087
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