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

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.

Scenario Prediction of China’s Natural Gas Consumption and Carbon Emissions in the Next Ten Years

Based on Johansen Cointegration Test, this paper sheds light on the long-run equilibrium relationship between natural gas consumption, gas production, and GDP in China. Three different natural gas demand scenarios of low, medium and high rates in the next ten years are considered, and a Neural Network Autoregression Model is used to predict the future carbon dioxide emission. We conclude: (1) In all three scenarios, the growth rates of natural gas consumption are all higher than those of natural gas production, while the gap between demand and domestic supply will gradually turn broader and China will largely rely on imports ; (2) In the scenario of low-rate economic growth, natural gas consumption will grow slowly, and it will be difficult to realize the carbon emission reduction targets by 2030 due to low-rate substitution of natural gas for coal; (3) If medium-rate to high-rate economic growth sustains, coupled with rapid increase in natural gas consumption and production, China’s Carbon Emission Reduction Targets for 2030 can be achieved with high-rate substitution of natural gas for coal.

Urban Household Energy Consumption Forecasting Based on Energy Price Impact Mechanism

The energy price influence system is one of the key mechanisms in the study of energy consumption. China’s household energy consumption has obvious regional differences, and rising income levels and urbanisation have changed the willingness and ability of households to make energy consumption choices. Based on the linear price effect of household energy consumption, this paper explores the scenario characteristics of energy prices affecting energy consumption, taking electricity and natural gas consumption as examples. Based on household energy consumption statistics from 2005 to 2018 in 36 major cities across China, the accuracy and change trends of household energy consumption forecasts are investigated through the decision tree-support vector machine (DT-SVR) non-linear forecasting technique. The study shows that the non-linear forecasting technique accurately portrays the predicted trends of changes in total urban household electricity and natural gas consumption. Within the less developed regions of economic development, income levels are still the main constraint on changes in urban household energy consumption, and the stimulating effect of income levels on household energy consumption has not been seen in the process of economic development in these less developed regions. Urbanisation as an important factor in examining household energy consumption, different development patterns and development processes will gradually be reflected in scenario aspects such as the choice of urban household energy consumption and changes in total consumption.

Strategic Potential of Agricultural Waste as a Feedstock for Biofuels Production in Ukraine

Ukraine’s agriculture is a leading sector of the national economy. Ukraine has a significant area (603628 km2), 70.9% of which are agricultural lands. Quality soil and good climatic conditions create favorable conditions for the development of crop and livestock production. The generation of a large amount of organic waste from agriculture opens wide opportunities for the development of the biogas technologies. The aim of the paper is to identify the main waste management trends in Ukraine based on data on waste generation and waste management and to calculate the strategic potential of agricultural waste as a feedstock for biofuels production. The resource potential of crop, livestock and processing waste has been considered and the necessity of its use for energy purposes has been substantiated. It has been determined that the greatest potential of agricultural waste that can be used for biogas production in Ukraine is concentrated in crop production. The livestock industry and processing enterprises also have a powerful feedstock base for biogas production. It has been determined that the agroindustrial sector of Ukraine produces significant amount of waste. As a result of the study, it has been found that the potential volume of biogas production from agricultural waste can replace 36.1% of natural gas consumption in Ukraine.

Role of Natural Gas Consumption in the Reduction of CO2 Emissions: Case of Azerbaijan

Azerbaijan signed the Paris Agreement in 2016 and committed to cut greenhouse gas (GHG) emissions by 35% in 2030. Meanwhile, natural gas has been vital component in the total energy mix of Azerbaijan economy and accounted for almost 65% of the total energy consumption. In the overall electricity mix, natural gas-fired power plants generate 93% of the country’s electricity. Since global energy consumption is responsible for 73% of human-caused greenhouse-gas emissions, and CO2 makes up more than 74% of the total, this study investigates possible mitigation effects of the natural gas consumption on CO2 emissions for Azerbaijan. Author employed several cointegration methodologies, namely Bound testing Autoregressive Distributed Lag (ARDL) approach, Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Structural Time Series model (STSM). Author of this paper found that when the share of natural gas increases 1 percent in the total energy mix, CO2 emission per capita decreases approximately 0.14 percent as a result of the ARDL, FMOLS, and DOLS models. All three models provide cointegration between the share of natural gas in the total energy mix and reduction in CO2 emissions.