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Forecasting Natural Gas Flows in Large Networks

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Machine Learning, Optimization, and Big Data (MOD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10710))

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Abstract

Natural gas is the cleanest fossil fuel since it emits the lowest amount of other remains after being burned. Over the years, natural gas usage has increased significantly. Accurate forecasting is crucial for maintaining gas supplies, transportation and network stability. This paper presents two methodologies to identify the optimal configuration o parameters of a Neural Network (NN) to forecast the next 24 h of gas flow for each node of a large gas network.

In particular the first one applies a Design Of Experiments (DOE) to obtain a quick initial solution. An orthogonal design, consisting of 18 experiments selected among a total of 4.374 combinations of seven parameters (training algorithm, transfer function, regularization, learning rate, lags, and epochs), is used. The best result is selected as initial solution of an extended experiment for which the Simulated Annealing is run to find the optimal design among 89.100 possible combinations of parameters.

The second technique is based on the application of Genetic Algorithm for the selection of the optimal parameters of a recurrent neural network for time series forecast. GA was applied with binary representation of potential solutions, where subsets of bits in the bit string represent different values for several parameters of the recurrent neural network.

We tested these methods on three municipal nodes, using one year and half of hourly gas flow to train the network and 60 days for testing. Our results clearly show that the presented methodologies bring promising results in terms of optimal configuration of parameters and forecast error.

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Acknowledgments

The authors would like to acknowledge networking support by the COST Action TD1207.

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Authors and Affiliations

  1. University of Modena and Reggio Emilia, Via Amendola 2, Pad. Morselli, 42122, Reggio Emilia, Italy

    Mauro Dell’Amico & Natalia Selini Hadjidimitriou

  2. Zuse Institute Berlin, Takustr. 7, 14195, Berlin, Germany

    Thorsten Koch & Milena Petkovic

Authors
  1. Mauro Dell’Amico
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  2. Natalia Selini Hadjidimitriou
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  3. Thorsten Koch
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  4. Milena Petkovic
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Corresponding author

Correspondence to Natalia Selini Hadjidimitriou .

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Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Dell’Amico, M., Hadjidimitriou, N.S., Koch, T., Petkovic, M. (2018). Forecasting Natural Gas Flows in Large Networks. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_14

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  • DOI: https://doi.org/10.1007/978-3-319-72926-8_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72925-1

  • Online ISBN: 978-3-319-72926-8

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