Computer Engineering Commons^™

Forecasting Design Day Demand Using Extremal Quantile Regression, David Joseph Kaftan, Jarrett L. Smalley, George F. Corliss, Ronald H. Brown, Richard James Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Extreme events occur rarely, making them difficult to predict. Extreme cold events strain natural gas systems to their limits. Natural gas distribution companies need to be prepared to satisfy demand on any given day that is at or warmer than an extreme cold threshold. The hypothetical day with temperature at this threshold is called the Design Day. To guarantee Design Day demand is satisfied, distribution companies need to determine the demand that is unlikely to be exceeded on the Design Day.

We approach determining this demand as an extremal quantile regression problem. We review current methods for extremal quantile regression. …