Gas load forecasting model input factor identification using a genetic algorithm

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 m...

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Bibliographic Details
Main Authors: Hui Li Lim, Brown, R.H.
Format: Conference Proceeding
Language:English
Subjects:
Costs
Demand forecasting
Economic forecasting
Gas industry
Genetic algorithms
Load forecasting
Load modeling
Natural gas
Natural gas industry
Predictive models
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Summary: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.
DOI:10.1109/MWSCAS.2001.986277