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    <title language="eng">Statistical analysis and modeling for detecting regime changes in gas nomination time series</title>
    <abstract language="eng">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.</abstract>
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    <author>Milena Petkovic</author>
    <submitter>Nazgul Zakiyeva</submitter>
    <author>Nazgul Zakiyeva</author>
    <author>Janina Zittel</author>
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      <title>ZIB-Report</title>
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    <collection role="persons" number="zittel">Zittel, Janina</collection>
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    <title language="eng">Modeling and forecasting gas network flows with multivariate time series and mathematical programming approach</title>
    <abstract language="eng">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.</abstract>
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    <author>Nazgul Zakiyeva</author>
    <submitter>Nazgul Zakiyeva</submitter>
    <author>Milena Petkovic</author>
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      <title>ZIB-Report</title>
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    <title language="eng">High-dimensional high-frequency time series prediction with a mixed integer optimisation method</title>
    <abstract language="deu">We study a functional autoregressive model for high-frequency time series. We approach the estimation of the proposed model using a Mixed Integer Optimisation method. The proposed model captures serial dependence in the functional time series by including high-dimensional curves. We illustrate our methodology on large-scale natural gas network data. Our model provides more accurate day-ahead hourly out-of-sample forecast of the gas in and out-flows compared to alternative prediction models.</abstract>
    <parentTitle language="eng">Operations Research Proceedings 2023. OR 2023</parentTitle>
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    <title language="eng">Modeling and Forecasting Gas Network Flows with Multivariate Time Series and Mathematical Programming Approach</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Operations Research Proceedings 2021. OR 2021.</parentTitle>
    <identifier type="doi">10.1007/978-3-031-08623-6_31</identifier>
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    <author>Nazgul Zakiyeva</author>
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    <author>Milena Petkovic</author>
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    <title language="eng">Statistical Analysis and Modeling for Detecting Regime Changes in Gas Nomination Time Series</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Operations Research Proceedings 2021. OR 2021</parentTitle>
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    <title language="eng">Large-scale functional network time series model solved with mathematical programming approach</title>
    <abstract language="eng">A functional network autoregressive model is proposed for studying large-scale network time series observed at high temporal resolution. The model incorporates high-dimensional curves to capture both serial and cross-sectional dependence in large-scale network functional time series. Estimation of the model is approached using a Mixed Integer Optimization method. Simulation studies confirm the consistency of parameter and adjacency matrix estimation. The method is applied to data from a real-life natural gas supply network. Compared to alternative prediction models, the proposed model delivers more accurate day-ahead hourly out-of-sample forecasts of the gas inflows and outflows at most gas nodes.</abstract>
    <parentTitle language="eng">Econometrics and Statistics</parentTitle>
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    <title language="eng">Large-scale functional network time series model solved with mathematical programming approach</title>
    <parentTitle language="eng">Econometrics and Statistics</parentTitle>
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