Based on the data collected over a two year time period, which included temperature, wind speed and gas consumption during the day, the effects of weather factors on gas consumption in the city have been established with the use of multiple regression. The impact of a particular month, day (dummy variable) or holiday of a year on the gas consumption has also been determined. The models of linear regression and artificial neural networks have been constructed for determining the gas consumption. An attempt has been made to find the best regression models and compare them to the neural network models with the use of mean absolute percentage error (MAPE).
The analyses scale of natural gas use for power generation in selected countries have been analyzed. In the USA natural gas has strengthened its position as fuel in the electricity generation sector, while in the EU member states opposite tendencies were observed. One of the main reasons for these differences are significantly higher prices of natural gas in EU member states in comparison to prices in the USA. The article examines the relationship between gross domestic product and natural gas consumption. Moreover, the costs of electricity obtained from different sources were compared, with particular emphasis on technologies based on natural gas.
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