TY - CHAP A1 - Petkovic, Milena A1 - Zakiyeva, Nazgul A1 - Zittel, Janina T1 - Statistical Analysis and Modeling for Detecting Regime Changes in Gas Nomination Time Series T2 - Operations Research Proceedings 2021. OR 2021 N2 - 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 h-ahead. In our study, we aim to investigate the regime changes in time series of nominations to predict the 6 to 10 h-ahead of gas nominations. Y1 - 2022 U6 - https://doi.org/10.1007/978-3-031-08623-6_29 SP - 188 EP - 193 PB - Springer, Cham ER - TY - CHAP A1 - Zakiyeva, Nazgul A1 - Petkovic, Milena T1 - Modeling and Forecasting Gas Network Flows with Multivariate Time Series and Mathematical Programming Approach T2 - Operations Research Proceedings 2021. OR 2021. N2 - With annual consumption of approx. 95 billion cubic meters and similar amounts of gas just transshipped through Germany to other EU states, Germany’s gas transport system plays a vital role in European energy supply. The complex, more than 40,000 km long high-pressure transmission network is controlled by several transmission system operators (TSOs) whose main task is to provide security of supply in a cost-efficient way. Given the slow speed of gas flows through the gas transmission network pipelines, it has been an essential task for the gas network operators to enhance the forecast tools to build an accurate and effective gas flow prediction model for the whole network. By incorporating the recent progress in mathematical programming and time series modeling, we aim to model natural gas network and predict gas in- and out-flows at multiple supply and demand nodes for different forecasting horizons. Our model is able to describe the dynamics in the network by detecting the key nodes, which may help to build an optimal management strategy for transmission system operators. Y1 - 2022 U6 - https://doi.org/10.1007/978-3-031-08623-6_31 SP - 200 EP - 205 PB - Springer, Cham ER -