Date of Award
Summer 2002
Document Type
Thesis - Restricted
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering
First Advisor
Brown, Ronald
Second Advisor
Povinelli, Richard
Third Advisor
Richie, James
Abstract
The focus of this work is on the development of hourly natural gas forecasting models for the purpose of providing hourly gas demand estimation to the utilities. Some utilities need to know hourly gas demand to economically and efficiently operate their gas distribution networks. Therefore, prediction of accurate natural gas demand can help significant cost savings for these utilities. The contributions of this research are the identification of input factors for the natural gas forecasting models and the development of these forecasting models using two techniques, linear regression and artificial neural network. First, input factors are identified and selected for the 106 (hour O to hour 105) natural gas forecasting models. based on identification methods such as genetic algorithm. Each of these 106 models has a unique set of input factors. For each set of the model input factors, a model for forecasting gas demand is then constructed using least squares and an artificial neural network. Both methods are demonstrated to efficiently identify the linear and nonlinear gas consumption problem.
Recommended Citation
Lim, Hui Li, "Computational Intelligence Models for Short Term Natural Gas Demand Forecasting" (2002). Master's Theses (1922-2009) Access restricted to Marquette Campus. 4996.
https://epublications.marquette.edu/theses/4996