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

•Statistical models for predicting grid stress using weather data are developed.•The relative importance of weather variables and observed time scale are evaluated.•Models fit to specific operation zones provide benefits over a globally-fitted model.•Temperature, absolute humidity, precipitation, and previous days’ precipitation are key predictive variables for all zones. 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.