Forecasting of natural gas consumption with artificial neural networks

Jolanta Szoplik

Energy, 2015, vol. 85, issue C, 208-220

Abstract: In this study, the results of forecasting of the gas demand obtained with the use of artificial neural networks are presented. Design and training of MLP (multilayer perceptron model) was carried out using data describing the actual natural gas consumption in Szczecin (Poland). In the model, calendar (month, day of month, day of week, hour) and weather (temperature) factors, which have a pronounced effect on gas consumption by individual consumers and small industry, were considered. The results of forecasts with the use of MLP models differing in the number of neurons in the hidden layer and in the size of the data set used in the training process were compared. MLP networks with the higher quality were used for the preparation of gas consumption forecast for the additional input data, which was not previously used in the training process. It was found that MLP 22-36-1 model can be successfully used to predict gas consumption on any day of the year and any hour of the day.

Keywords: Natural gas consumption; Forecasting; Artificial neural network (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (95)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:85:y:2015:i:c:p:208-220

DOI: 10.1016/j.energy.2015.03.084

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