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
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;
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
DOI :
10.1109/MWSCAS.2001.986277
