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 - 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 -