DocumentCode
2290439
Title
Gas load forecasting model input factor identification using a genetic algorithm
Author
Lim, Hui Li ; Brown, Ronald H.
Author_Institution
Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA
Volume
2
fYear
2001
fDate
2001
Firstpage
670
Abstract
Genetic algorithms (GAs) are used as a tool to identify the input factors for an hourly gas load forecasting model. The proposed model can provide up to 106 hours of load forecasts. Experiences obtained during the application of GA for determination of inputs are discussed. Linear regression based models using the results of this study had an average error 23% less than the existing method at one gas utility over six service areas
Keywords
forecasting theory; genetic algorithms; identification; public utilities; statistical analysis; 106 hour; gas load forecasting model; gas utility; genetic algorithm; input factor identification; linear regression based models; service areas; Costs; Demand forecasting; Economic forecasting; Gas industry; Genetic algorithms; Load forecasting; Load modeling; Natural gas; Natural gas industry; Predictive models;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2001. MWSCAS 2001. Proceedings of the 44th IEEE 2001 Midwest Symposium on
Conference_Location
Dayton, OH
Print_ISBN
0-7803-7150-X
Type
conf
DOI
10.1109/MWSCAS.2001.986277
Filename
986277
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