Natural gas consumption forecasting: A discussion on forecasting history and future challenges

Liu, Jinyuan; Wang, Shouxi; Wei, Nan; Chen, Xi; Xie, Hanyu; Wang, Jie

doi:10.1016/j.jngse.2021.103930

Cited by 77 publications

(31 citation statements)

References 94 publications

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“…Therefore this variable describes the relationship between fossil fuels and renewables as gas which will become the dominant fossil fuel in more and more countries. This approach is consistent with the literature [46]. While alternative energy sources and nuclear power are listed in this paper as potential variables for long-term forecasting, in fact these variables were only used in one study [66].…”

Section: Data Descriptionsupporting

confidence: 56%

“…It is commonly expressed in the literature that the accuracy of forecasts in each horizon can be significantly improved by aggregating forecasts made with different methods [43,44]. For gas consumption, a typical combination of forecasts is a mixture of regression and neural networks [45,46]. Regression extrapolates trends better, and neural networks give better estimation of changes in the behavior of the forecasted object over time.…”

Section: Literature Reviewmentioning

confidence: 99%

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Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

Gaweł

Paliński

2021

Energies

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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.

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Section: Data Descriptionsupporting

confidence: 56%

Section: Literature Reviewmentioning

confidence: 99%

Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

Gaweł

Paliński

2021

Energies

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“…Due to the development of modern control theory and the introduction of various mathematical methods, especially the widespread application of computers and calculation techniques, the power angle stability has entered a high level of understanding, and various analysis and control methods have become more mature and have been practically applied. So far, the research on the stability of the power angle has achieved recognized and impressive results [12][13][14].…”

Section: Related Workmentioning

confidence: 99%

A Forecast Model of City Natural Gas Daily Load Based on Data Mining

Chen

Zhang

2022

Scientific Programming

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Data mining technology is more and more widely used in the daily load forecasting of natural gas systems. It is still difficult to carry out high-precision, timely intraday load forecasting and intraday load dynamic characteristics clustering for natural gas systems. Based on data mining technology, this paper proposes a stable intraday load forecasting method for the natural gas flow state-space model. The load sensitivity under the current operating conditions of the system is obtained by calculation; the sample space of the state space is established through data processing; the partitions under different clustering radii are calculated; and the best intraday load flow is obtained through the state space effectiveness evaluation method. The experimental results show that the model load forecasting accuracy and relative error reached 98.5% and 0.026, respectively, which solved the problem of processing the long-term accumulated historical data of gas intra-day load. At the same time, the amount of data calculation was reduced by 33.6%, which effectively promoted the quantification of intraday load influencing factors and qualitative analysis.

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“…The most used energy management variables are natural gas, all of which are imported. Evaluating natural gas consumption such as annually, monthly, weekly, daily, and hourly, will lead to the management of plans such as transportation, distribution, and natural gas production [16,17], along with sampling frequency, such as yearly, daily, and hourly [18,19]. In daily studies, NG demand was predicted.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models

et al. 2021

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Research on forecasting the seasonality and growth trend of natural gas (NG) production and consumption will help organize an analysis base for NG inspection and development, social issues, and allow industrials elements to operate effectively and reduce economic issues. In this situation, we handle a comparison structure on the application of different models in monthly NG production and consumption forecasting using the cross-correlation function and then analyze the association between exogenous variables. Moreover, the SARIMA-X model is tested for US monthly NG production and consumption prediction via the proposed method for the first time in the literature review in this study. The performance of that model has been compared with SARIMA (p, d, q) * (P, D, Q)s. The results from RMSE and MAPE indicate that the superiority of the best model. By applying this method, the US monthly NG production and consumption is forecast until 2025. The success of the proposed method allows the use of seasonality patterns. If this seasonal approach continues, the United States’ NG production (16%) and consumption (24%) are expected to increase by 2025. The results of this study provide effective information for decision-makers on NG production and consumption to be credible and to determine energy planning and future sustainable energy policies.

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Natural gas consumption forecasting: A discussion on forecasting history and future challenges

Cited by 77 publications

References 94 publications

Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

A Forecast Model of City Natural Gas Daily Load Based on Data Mining

Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models

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