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  • 1
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    Statistical Analysis and Modeling for Detecting Regime Changes in Gas Nomination Time Series (2022)
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    Publication Date: 2022-12-06
    Description: 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.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 2
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    Mathematical Optimization for Analyzing and Forecasting Nonlinear Network Time Series (2022)
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    Publication Date: 2023-01-09
    Description: This work presents an innovative short to mid-term forecasting model that analyzes nonlinear complex spatial and temporal dynamics in energy networks under demand and supply balance constraints using Network Nonlinear Time Series (TS) and Mathematical Programming (MP) approach. We address three challenges simultaneously, namely, the adjacency matrix is unknown; the total amount in the network has to be balanced; dependence is unnecessarily linear. We use a nonparametric approach to handle the nonlinearity and estimate the adjacency matrix under the sparsity assumption. The estimation is conducted with the Mathematical Optimisation method. We illustrate the accuracy and effectiveness of the model on the example of the natural gas transmission network of one of the largest transmission system operators (TSOs) in Germany, Open Grid Europe. The obtained results show that, especially for shorter forecasting horizons, proposed method outperforms all considered benchmark models, improving the avarage nMAPE for 5.1% and average RMSE for 79.6% compared to the second-best model. The model is capable to capture the nonlinear dependencies in the complex spatial-temporal network dynamics and benefits from both sparsity assumption and the demand and supply balance constraint.
    Language: English
    Type: article , doc-type:article
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    OPUS
  • 3
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    Modeling and Forecasting Gas Network Flows with Multivariate Time Series and Mathematical Programming Approach (2022)
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    Publication Date: 2023-08-02
    Description: 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.
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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    OPUS
  • 4
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    Article's Scientific Prestige: Measuring the Impact of Individual Articles in the Web of Science (2022)
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    Publication Date: 2023-12-19
    Description: We performed a citation analysis on the Web of Science publications consisting of more than 63 million articles and 1.45 billion citations on 254 subjects from 1981 to 2020. We proposed the Article’s Scientific Prestige (ASP) metric and compared this metric to number of citations (#Cit) and journal grade in measuring the scientific impact of individual articles in the large-scale hierarchical and multi-disciplined citation network. In contrast to #Cit, ASP, that is computed based on the eigenvector centrality, considers both direct and indirect citations, and provides steady-state evaluation cross different disciplines. We found that ASP and #Cit are not aligned for most articles, with a growing mismatch amongst the less cited articles. While both metrics are reliable for evaluating the prestige of articles such as Nobel Prize winning articles, ASP tends to provide more persuasive rankings than #Cit when the articles are not highly cited. The journal grade, that is eventually determined by a few highly cited articles, is unable to properly reflect the scientific impact of individual articles. The number of references and coauthors are less relevant to scientific impact, but subjects do make a difference.
    Language: English
    Type: article , doc-type:article
    Format: application/pdf
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    OPUS
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