TY - JOUR A1 - Petkovic, Milena A1 - Chen, Ying A1 - Gamrath, Inken A1 - Gotzes, Uwe A1 - Hadjidimitrou, Natalia Selini A1 - Zittel, Janina A1 - Xu, Xiaofei A1 - Koch, Thorsten T1 - A hybrid approach for high precision prediction of gas flows JF - Energy Systems N2 - About 23% of the German energy demand is supplied by natural gas. Additionally, for about the same amount Germany serves as a transit country. Thereby, the German network represents a central hub in the European natural gas transport network. The transport infrastructure is operated by transmissions system operators (TSOs). The number one priority of the TSOs is to ensure the security of supply. However, the TSOs have only very limited knowledge about the intentions and planned actions of the shippers (traders). Open Grid Europe (OGE), one of Germany’s largest TSO, operates a high-pressure transport network of about 12,000 km length. With the introduction of peak-load gas power stations, it is of great importance to predict in- and out-flow of the network to ensure the necessary flexibility and security of supply for the German Energy Transition (“Energiewende”). In this paper, we introduce a novel hybrid forecast method applied to gas flows at the boundary nodes of a transport network. This method employs an optimized feature selection and minimization. We use a combination of a FAR, LSTM and mathematical programming to achieve robust high-quality forecasts on real-world data for different types of network nodes. Y1 - 2022 U6 - https://doi.org/10.1007/s12667-021-00466-4 VL - 13 SP - 383 EP - 408 ER - TY - GEN A1 - Chen, Ying A1 - Koch, Thorsten A1 - Xu, Xiaofei T1 - Regularized partially functional autoregressive model with application to high-resolution natural gas forecasting in Germany N2 - We propose a partially functional autoregressive model with exogenous variables (pFAR) to describe the dynamic evolution of the serially correlated functional data. It provides a unit� ed framework to model both the temporal dependence on multiple lagged functional covariates and the causal relation with ultrahigh-dimensional exogenous scalar covariates. Estimation is conducted under a two-layer sparsity assumption, where only a few groups and elements are supposed to be active, yet without knowing their number and location in advance. We establish asymptotic properties of the estimator and investigate its unite sample performance along with simulation studies. We demonstrate the application of pFAR with the high-resolution natural gas flows in Germany, where the pFAR model provides insightful interpretation as well as good out-of-sample forecast accuracy. T3 - ZIB-Report - 19-34 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-74880 SN - 1438-0064 ER - TY - JOUR A1 - Chen, Ying A1 - Koch, Thorsten A1 - Zakiyeva, Nazgul A1 - Liu, Kailiang A1 - Xu, Zhitong A1 - Chen, Chun-houh A1 - Nakano, Junji A1 - Honda, Keisuke T1 - Article’s scientific prestige: Measuring the impact of individual articles in the web of science JF - Journal of Informetrics N2 - 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. Y1 - 2023 U6 - https://doi.org/10.1016/j.joi.2023.101379 VL - 17 IS - 1 SP - 101379 ER - TY - JOUR A1 - Chen, Ying A1 - Chua, Wee Song A1 - Koch, Thorsten T1 - Forecasting day-ahead high-resolution natural-gas demand and supply in Germany JF - Applied Energy N2 - Forecasting natural gas demand and supply is essential for an efficient operation of the German gas distribution system and a basis for the operational decisions of the transmission system operators. The German gas market is moving towards more short-term planning, in particular, day-ahead contracts. This increases the difficulty that the operators in the dispatching centre are facing, as well as the necessity of accurate forecasts. This paper presents a novel predictive model that provides day-ahead forecasts of the high resolution gas flow by developing a Functional AutoRegressive model with eXogenous variables (FARX). The predictive model allows the dynamic patterns of hourly gas flows to be described in a wide range of historical profiles, while also taking the relevant determinants data into account. By taking into account a richer set of information, FARX provides stronger performance in real data analysis, with both accuracy and high computational efficiency. Compared to several alternative models in out-of-sample forecasts, the proposed model can improve forecast accuracy by at least 12% and up to 5-fold for one node, 3% to 2-fold and 2-fold to 4-fold for the other two nodes. The results show that lagged 1-day gas flow and nominations are important predictors, and with their presence in the forecast model, temperature becomes insignificant for short-term predictions. Y1 - 2018 U6 - https://doi.org/https://doi.org/10.1016/j.apenergy.2018.06.137 IS - Volume 228 SP - 1091 EP - 1110 ER - TY - JOUR A1 - Chen, Ying A1 - Xu, Xiuqin A1 - Koch, Thorsten T1 - Day-ahead high-resolution forecasting of natural gas demand and supply in Germany with a hybrid model JF - Applied Energy N2 - As the natural gas market is moving towards short-term planning, accurate and robust short-term forecasts of the demand and supply of natural gas is of fundamental importance for a stable energy supply, a natural gas control schedule, and transport operation on a daily basis. We propose a hybrid forecast model, Functional AutoRegressive and Convolutional Neural Network model, based on state-of-the-art statistical modeling and artificial neural networks. We conduct short-term forecasting of the hourly natural gas flows of 92 distribution nodes in the German high-pressure gas pipeline network, showing that the proposed model provides nice and stable accuracy for different types of nodes. It outperforms all the alternative models, with an improved relative accuracy up to twofold for plant nodes and up to fourfold for municipal nodes. For the border nodes with rather flat gas flows, it has an accuracy that is comparable to the best performing alternative model. KW - Natural gas flow forecasting KW - Neural network KW - Hybrid model KW - Functional autoregressive Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1016/j.apenergy.2019.114486 VL - 262 IS - 114486 ER - TY - GEN A1 - Petkovic, Milena A1 - Chen, Ying A1 - Gamrath, Inken A1 - Gotzes, Uwe A1 - Hadjidimitriou, Natalia Selini A1 - Zittel, Janina A1 - Xu, Xiaofei A1 - Koch, Thorsten T1 - A Hybrid Approach for High Precision Prediction of Gas Flows N2 - About 20% of the German energy demand is supplied by natural gas. Ad- ditionally, for about twice the amount Germany serves as a transit country. Thereby, the German network represents a central hub in the European natural gas transport network. The transport infrastructure is operated by so-called transmissions system operators or TSOs. The number one priority of the TSOs is to ensure security of supply. However, the TSOs have no knowledge of the intentions and planned actions of the shippers (traders). Open Grid Europe (OGE), one of Germany’s largest TSO, operates a high- pressure transport network of about 12.000 km length. Since flexibility and security of supply is of utmost importance to the German Energy Transition (“Energiewende”) especially with the introduction of peak-load gas power stations, being able to predict in- and out-flow of the network is of great importance. In this paper we introduce a new hybrid forecast method applied to gas flows at the boundary nodes of a transport network. The new method employs optimized feature minimization and selection. We use a combination of an FAR, LSTM DNN and mathematical programming to achieve robust high quality forecasts on real world data for different types of network nodes. Keywords: Gas Forecast, Time series, Hybrid Method, FAR, LSTM, Mathematical Optimisation T3 - ZIB-Report - 19-26 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-73525 SN - 1438-0064 ER - TY - JOUR A1 - Chen, Ying A1 - Koch, Thorsten A1 - Zakiyeva, Nazgul A1 - Liu, Kailiang A1 - Xu, Zhitong A1 - Chen, Chun-houh A1 - Nakano, Junji A1 - Honda, Keisuke T1 - Article's Scientific Prestige: Measuring the Impact of Individual Articles in the Web of Science N2 - 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. T3 - ZIB-Report - 22-07 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-86380 VL - 17 IS - 1 SP - 101379 ER - TY - JOUR A1 - Chen, Ying A1 - Zakiyeva, Nazgul A1 - Zhu, Bangzhu A1 - Koch, Thorsten T1 - Modeling and Forecasting the Dynamics of the Natural Gas Transmission Network in Germany with the Demand and Supply Balance Constraint JF - Applied Energy Y1 - 2020 U6 - https://doi.org/10.1016/j.apenergy.2020.115597 N1 - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3646636 IS - 278 ER - TY - JOUR A1 - Koch, Thorsten A1 - Chen, Ying A1 - Lim, Kian Guan A1 - Xu, Xiaofei A1 - Zakiyeva, Nazgul T1 - A review study of functional autoregressive models with application to energy forecasting JF - WIREs Computational Statistics Y1 - 2020 U6 - https://doi.org/10.1002/wics.1525 N1 - https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=7693&context=lkcsb_research ER - TY - JOUR A1 - Xu, Xiaofei A1 - Chen, Ying A1 - Zhang, Ge A1 - Koch, Thorsten T1 - Modeling functional time series and mixed-type predictors with partially functional autoregressions* JF - Journal of Business and Economic Statistics N2 - In many business and economics studies, researchers have sought to measure the dynamic dependence of curves with high-dimensional mixed-type predictors. We propose a partially functional autoregressive model (pFAR) where the serial dependence of curves is controlled by coefficient operators that are defined on a two-dimensional surface, and the individual and group effects of mixed-type predictors are estimated with a two-layer regularization. We develop an efficient estimation with the proven asymptotic properties of consistency and sparsity. We show how to choose the sieve and tuning parameters in regularization based on a forward-looking criterion. In addition to the asymptotic properties, numerical validation suggests that the dependence structure is accurately detected. The implementation of the pFAR within a real-world analysis of dependence in German daily natural gas flow curves, with seven lagged curves and 85 scalar predictors, produces superior forecast accuracy and an insightful understanding of the dynamics of natural gas supply and demand for the municipal, industry, and border nodes, respectively. Y1 - 2021 U6 - https://doi.org/10.1080/07350015.2021.2011299 SP - 1 EP - 43 ER -