 Day-ahead high-resolution forecasting of natural gas demand and supply in Germany with a hybrid modelYing Chen, Xiuqin Xu and Thorsten Koch Applied Energy, 2020, vol. 262, issue C, No S0306261919321749 Abstract: 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. Keywords: Natural gas flow forecasting; Functional autoregressive; Neural network; Hybrid model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:262:y:2020:i:c:s0306261919321749 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2019.114486 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
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