Nonlinear and Spline Regression Models for Forecasting Gas Flow on Exits of Gas Transmission Networks
نویسندگان
چکیده
The flow of natural gas within a gas transmission network is studied with the aim to predict gas loads for very low temperatures. Two models for describing dependence between the maximal daily gas flow and the temperature on network exits are presented. A Brain-Cousens regression model is chosen from the class of parametric models. As an alternative, a semi-parametric logistic regression based on penalized splines is considered. The comparison of prediction based on both models is included.
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تاریخ انتشار 2011