Date of Award
Fall 2004
Document Type
Thesis - Restricted
Degree Name
Master of Science (MS)
Department
Civil, Construction, and Environmental Engineering
First Advisor
Brown, Ronald H.
Second Advisor
Richie, James
Third Advisor
Povinelli, Richard
Abstract
The focus of this work is on the selection of inputs for combining daily natural gas demand forecast models. One main problem of selecting the inputs is that each utility has different characteristics and sensitivity to inputs. Finding a set of inputs that works well on every utility is desired; however, it is not an easy task due to the diversity of the utilities. The contributions of this research are the development to reduce the multicollinearity problem in the current linear regression model of GASDAYtm, the use of the weekend and/or weekday indicators to help simulate the weekly period of the daily gas demand, and the use of more error terms as exogenous inputs to improve the performance of the forecast model. There are six new forecasting models developed in this study. These new models are simulated on six utilities with different characteristics. All the new forecasting models developed in this study performed well; furthermore, one of them is shown to considerably improve the performance of forecasting daily natural gas demand.
Recommended Citation
Halim, Susanto, "Selection of Inputs for Generating Combinatorial Daily Natural Gas Demand Forecasts" (2004). Master's Theses (1922-2009) Access restricted to Marquette Campus. 4816.
https://epublications.marquette.edu/theses/4816