Natural Gas Consumption Forecasting Based on the Variability of External Meteorological Factors Using Machine Learning Algorithms

Wojciech Panek and Tomasz Włodek
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Wojciech Panek: Independent Expert, formerly Polish Natural Gas Distribution Operator-PSG Sp z o.o., Bandrowskiego 16, PL33100 Tarnów, Poland
Tomasz Włodek: Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Al. Mickiewicza 30, PL30059 Krakow, Poland

Energies, 2022, vol. 15, issue 1, 1-19

Abstract: Natural gas consumption depends on many factors. Some of them, such as weather conditions or historical demand, can be accurately measured. The authors, based on the collected data, performed the modeling of temporary and future natural gas consumption by municipal consumers in one of the medium-sized cities in Poland. For this purpose, the machine learning algorithms, neural networks and two regression algorithms, MLR and Random Forest were used. Several variants of forecasting the demand for natural gas, with different lengths of the forecast horizon are presented and compared in this research. The results obtained using the MLR, Random Forest, and DNN algorithms show that for the tested input data, the best algorithm for predicting the demand for natural gas is RF. The differences in accuracy of prediction between algorithms were not significant. The research shows the differences in the impact of factors that create the demand for natural gas, as well as the accuracy of the prediction for each algorithm used, for each time horizon.

Keywords: natural gas consumption; forecasting; random forest; neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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