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Gas Flow Prediction using Long Short-Term Memory Networks

Withagen, Maikel (2018) Gas Flow Prediction using Long Short-Term Memory Networks. Master's Thesis / Essay, Artificial Intelligence.

MA 1867733 MLC Withagen.pdf

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The organization of natural gas flows through an international gas transport network is a very complex and abstract process. Due to the slow, flowing aspect of compressed gas, operations on the network’s organization take some time to show their effects, and an intricate balance has to be struck between multiple factors. The aim of this study, was to find if LSTM networks were able to model such time series, and can therefore be used as predictive models for the gas flows in the network. The LSTM networks were able to sufficiently model all the possible components of a time series, if certain conditions were met. When modelling the national usage-based network, LSTM networks were able to achieve better results than the default time series regression technique ARIMA, and the heuristic currently used by the Gasunie. An important factor in achieving these results, was the use of an autoencoder layer, which allowed us to input the intrinsic network state to the LSTM model, while negating the naivety problem. A model of the complete international model gave imperfect results however, where the non-existent influence of model parameters on the performance indicated that the network most likely was lacking information. Further research is recommended to extend the model with information-rich features such as international natural gas prices on the stock markets, or expanding the complexity of the network’s topology.

Item Type: Thesis (Master's Thesis / Essay)
Supervisor name: Schomaker, L.R.B.
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 12 Aug 2018
Last Modified: 13 Aug 2018 08:58

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