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Predicting natural gas consumption by neural networks

Zlatko Tonković ; HEP - Plin d.o.o., HR-31000 Osijek, Hrvatska
Marijana Zekić-Sušac ; Sveučilište J. J. Strossmayera u Osijeku, Ekonomski fakultet u Osijeku, HR-31000 Osijek, Hrvatska
Marija Somolanji ; HEP - Plin d.o.o., HR-31000 Osijek, Hrvatska

Puni tekst: engleski pdf 16.756 Kb

str. 51-61

preuzimanja: 680



The aim of the paper is to create a prediction model of natural gas consumption on a regional level by using neural networks, and to analyze the results in order to improve prediction accuracy in further research. The output variable consisted of the next-day gas consumption in hourly intervals, while the input space included previous-day consumption in addition to exogenous variables. After conducting a feature selection procedure, two neural network algorithms were trained and tested: the multilayer perceptron and the radial basis function network with different activation functions. The dataset consisted of real historical data of a Croatian gas distributor. The best neural network model is selected on the basis of the mean absolute percentage error obtained on the test sample. The
results were analyzed, and some critical hours and days were identified. Guidelines were reported that could be valuable to both researchers and practitioners in this area.

Ključne riječi

natural gas consumption, neural networks, multilayer perceptron, radial basis function network, fuzzy variable

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Podaci na drugim jezicima: hrvatski

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