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 24h 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.

Forecasting natural gas flows in large networks / Dell'Amico, M.; Hadjidimitriou, N. S.; Koch, T.; Petkovic, M.. - 10710:(2018), pp. 158-171. (Intervento presentato al convegno 3rd International Conference on Machine Learning, Optimization, and Big Data, MOD 2017 tenutosi a ita nel 2017) [10.1007/978-3-319-72926-8_14].

Forecasting natural gas flows in large networks

Dell'Amico M.;Hadjidimitriou N. S.;
2018

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 24h 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.
2018
3rd International Conference on Machine Learning, Optimization, and Big Data, MOD 2017
ita
2017
10710
158
171
Dell'Amico, M.; Hadjidimitriou, N. S.; Koch, T.; Petkovic, M.
Forecasting natural gas flows in large networks / Dell'Amico, M.; Hadjidimitriou, N. S.; Koch, T.; Petkovic, M.. - 10710:(2018), pp. 158-171. (Intervento presentato al convegno 3rd International Conference on Machine Learning, Optimization, and Big Data, MOD 2017 tenutosi a ita nel 2017) [10.1007/978-3-319-72926-8_14].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1181231
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