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The traditional linear regression model that assumes normal residuals is applied extensively in engineering and science. However, the normality assumption of the model residuals is
often ineffective. This drawback can be overcome by using a generalized normal regression model
that assumes a non-normal response. In this paper, we propose regression models based on generalizations of the normal distribution. The proposed regression models can be used effectively in
modeling data with a highly skewed response. Furthermore, we study in some details the structural
properties of the proposed generalizations of the normal distribution. The maximum likelihood
method is used for estimating the parameters of the proposed method. The performance of the
maximum likelihood estimators in estimating the distributional parameters is assessed through a
small simulation study. Applications to two real datasets are given to illustrate the flexibility and the
usefulness of the proposed distributions and their regression models.
With annual consumption of approx. 95 billion cubic me-ters and similar amounts of gas just transshipped through Germany toother EU states, Germany’s gas transport system plays a vital role inEuropean energy supply. The complex, more than 40,000 km long high-pressure transmission network is controlled by several transmission sys-tem operators (TSOs) whose main task is to provide security of supplyin a cost-efficient way. Given the slow speed of gas flows through the gastransmission network pipelines, it has been an essential task for the gasnetwork operators to enhance the forecast tools to build an accurate andeffective gas flow prediction model for the whole network. By incorpo-rating the recent progress in mathematical programming and time seriesmodeling, we aim to model natural gas network and predict gas in- andout-flows at multiple supply and demand nodes for different forecastinghorizons. Our model is able to describe the dynamics in the network bydetecting the key nodes, which may help to build an optimal manage-ment strategy for transmission system operators.
As a result of the legislation for gas markets introduced by the European Union in 2005, separate independent companies have to conduct the transport and trading of natural gas. The current gas market of Germany, which has a market value of more than 54 billion USD, consists of Transmission System Operators (TSO), network users, and traders. Traders can nominate a certain amount of gas anytime and anywhere in the network. Such unrestricted access for the traders, on the other hand, increase the uncertainty in the gas supply management. Some customers’ behaviors may cause abrupt structural changes in gas flow time series. In particular, it is a challenging task for the TSO operators to predict gas nominations 6 to 10 hours ahead. In our study, we aim to investigate the regime changes in the time series of nominations to predict the 6 to 10 hours ahead of gas nominations.