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Estimating The Extreme Low-Temperature Event Using Nonparametric Methods, Anisha D'Silva
Estimating The Extreme Low-Temperature Event Using Nonparametric Methods, Anisha D'Silva
Master's Theses (2009 -)
This thesis presents a new method of estimating the one-in-N low temperature threshold using a non-parametric statistical method called kernel density estimation applied to daily average wind-adjusted temperatures. We apply our One-in-N Algorithm to local gas distribution companies (LDCs), as they have to forecast the daily natural gas needs of their consumers. In winter, demand for natural gas is high. Extreme low temperature events are not directly related to an LDCs gas demand forecasting, but knowledge of extreme low temperatures is important to ensure that an LDC has enough capacity to meet customer demands when extreme low temperatures are experienced. …