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.
Highlights
X axis has 2000 random values predicted by the Multiple Linear Regression (MLR) and Random Forest (RF) algorithms, and the Y axis is the actual gas consumption values, for the same input data for daily gas consumption
Results for Random Forest regression show that the predicted values are closer to the real values, their scatter is smaller
Values predicted by the MLR deviate significantly from actual values, while values predicted by the Random Forest are less inaccurate, as well as Deep Neural Network (DNN)
Summary
The Importance of Natural Gas Demand Prediction. Natural gas is one of the most important sources of energy. Generation of 1 MJ of energy from it produces the lowest amount of CO2 among all fossil fuels [1]. Easy distribution and storage determines its safe use. It is seen as the so-called transition fuel that will help to bring about the energy transition in developed countries [2]. In Poland, the use of natural gas in industry, e.g., for heating purposes or in technological processes, is cheaper and more environmentally friendly than the use of electricity produced mainly from coal for these purposes. The energy market is not indifferent to these properties of “blue fuel”
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