Design Day Analysis - Forecasting Extreme Daily Natural Gas Demand

This work provides a framework for Design Day analysis. First, we estimate the temperature conditions which are expected to be colder than all but one day in N years. This temperature is known as the Design Day condition. Then, we forecast an upper bound on natural gas demand when temperature is at the Design Day condition. Natural gas distribution companies (LDCs) need to meet demand during extreme cold days. Just as bridge builders design for a nominal load, natural gas distribution companies need to design for a nominal temperature. This nominal temperature is the Design Day condition. The Design Day condition is the temperature that is expected to be colder than every day except one in N years. Once Design Day conditions are estimated, LDCs need to prepare for the Design Day demand. We provide an upper bound on Design Day demand to ensure LDCs will be able to meet demand. Design Day conditions are determined in a variety of ways. First, we fit a kernel density function to surrogate temperatures - this method is referred to as the Surrogate Kernel Density Fit. Second, we apply Extreme Value Theory - a field dedicated to finding the maxima or minima of a distribution. In particular, we apply Block-Maxima and Peak-Over-Threshold (POT) techniques. The upper bound of Design Day demand is determined using a modified version of quantile regression. Similar Design Day conditions are estimated by both the Surrogate Kernel Density Fit and Peaks-Over-Threshold methods. Both methods perform well. The theory supporting the POT method and the empirical performance of the SKDF method lends confidence in the Design Day conditions estimates. The upper bound of demand on these conditions is well modeled by the modified quantile regression technique

Forecasting Design Day Demand Using Extremal Quantile Regression

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. We implement a quantile forecast to estimate the demand that has a minimal chance of being exceeded on the design day. We show extremal quantile regression to be more reliable than direct quantile estimation. We discuss the difficult task of evaluating a probabilistic forecast on rare events. Probabilistic forecasting is a quickly growing research topic in the field of energy forecasting. Our paper contributes to this field in three ways. First, we forecast quantiles during extreme cold events where data is sparse. Second, we forecast extremely high quantiles that have a very low probability of being exceeded. Finally, we provide a real world scenario on which to apply these techniques