Abstract
Classic forecasting methods of natural gas consumption extrapolate trends from the past to subsequent periods of time. The paper presents a different approach that uses analogues to create long-term forecasts of the annual natural gas consumption. The energy intensity (energy consumption per dollar of Gross Domestic Product—GDP) and gas share in energy mix in some countries, usually more developed, are the starting point for forecasts of other countries in the later period. The novelty of the approach arises in the use of cluster analysis to create similar groups of countries and periods based on two indicators: energy intensity of GDP and share of natural gas consumption in the energy mix, and then the use of fuzzy decision trees for classifying countries in different years into clusters based on several other economic indicators. The final long-term forecasts are obtained with the use of fuzzy decision trees by combining the forecasts for different fuzzy sets made by the method of relative chain increments. The forecast accuracy of our method is higher than that of other benchmark methods. The proposed method may be an excellent tool for forecasting long-term territorial natural gas consumption for any administrative unit.
Highlights
Forecasting the consumption of energy has been an important research topic for many decades
The quality of electricity consumption forecasts may vary depending on the group of consumers and the MAPE error variance, for example, from 2% to 10% for short-term forecasts, while the MAPE error for long-term forecasts is from 4% to 32% [2]
The following approach was used to make forecasts of natural gas consumption for all countries included in the dataset
Summary
Forecasting the consumption of energy has been an important research topic for many decades. Its role has been growing in proportion to the increase in energy demand in all countries of the world. For electricity markets, the quality of short-term and medium-term forecasts obtained with the use of various models is relatively good, because, for example, the typical forecasting error of monthly electricity consumption for one geographical area, measured by the mean absolute percentage error MAPE, is about 2% [1]. The quality of electricity consumption forecasts may vary depending on the group of consumers and the MAPE error variance, for example, from 2% to 10% for short-term forecasts, while the MAPE error for long-term forecasts is from 4% to 32% [2]
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