scholarly journals Forecasting Natural Gas Consumption of China Using a Novel Grey Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Chengli Zheng ◽  
Wen-Ze Wu ◽  
Jianming Jiang ◽  
Qi Li
Keyword(s):  
Natural Gas ◽  
Prediction Accuracy ◽  
Prediction Performance ◽  
Grey Model ◽  
Energy Market ◽  
Original Series ◽  
Proposed Model ◽  
Grey Models ◽  
Gas Consumption ◽  
Natural Gas Consumption

As is known, natural gas consumption has been acted as an extremely important role in energy market of China, and this paper is to present a novel grey model which is based on the optimized nonhomogeneous grey model (ONGM (1,1)) in order to accurately predict natural gas consumption. This study begins with proving that prediction results are independent of the first entry of original series using the product theory of determinant; on this basis, it is a reliable approach by inserting an arbitrary number in front of the first entry of original series to extract messages, which has been proved that it is an appreciable approach to increase prediction accuracy of the traditional grey model in the earlier literature. An empirical example often appeared in testing for prediction accuracy of the grey model is utilized to demonstrate the effectiveness of the proposed model; the numerical results indicate that the proposed model has a better prediction performance than other commonly used grey models. Finally, the proposed model is applied to predict China’s natural gas consumption from 2019 to 2023 in order to provide some valuable information for energy sectors and related enterprises.

scholarly journals A Novel Discrete Grey Model and Its Application

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Wen-Ze Wu ◽  
Jianming Jiang ◽  
Qi Li
Keyword(s):  
Tensile Strength ◽  
Arbitrary Number ◽  
Prediction Accuracy ◽  
Historical Data ◽  
Prediction Performance ◽  
Grey Model ◽  
Original Series ◽  
Proposed Model ◽  
Grey Models ◽  
Future Data

This paper aims to further increase the prediction accuracy of the grey model based on the existing discrete grey model, DGM(1,1). Herein, we begin by studying the connection between forecasts and the first entry of the original series. The results comprehensively show that the forecasts are independent of the first entry in the original series. On this basis, an effective method of inserting an arbitrary number in front of the first item of the original series to extract messages is applied to produce a novel grey model, which is abbreviated as FDGM(1,1) for simplicity. Incidentally, the proposed model can even forecast future data using only three historical data. To demonstrate the effectiveness of the proposed model, two classical examples of the tensile strength and life of the product are employed in this paper. The numerical results indicate that FDGM(1,1) has a better prediction performance than most commonly used grey models.

scholarly journals An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiming Hu ◽  
Chong Liu
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Prediction Performance ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Proposed Model ◽  
Grey Models ◽  
High Prediction

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of the NEGM (1, 1, t2) model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM (1, 1, t2). Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM (1, 1, t2) model has the best prediction performance among these competitive models.

scholarly journals Forecasting residential natural gas consumption in Egypt

2020 ◽  
Vol 2 (4) ◽  
pp. 297-308
Author(s):  
Mohamed Ali Ismail ◽  
Eman Mahmoud Abd El-Metaal
Keyword(s):  
Natural Gas ◽  
Moving Average ◽  
Percentage Error ◽  
Seasonal Patterns ◽  
Content Type ◽  
Cost Of Energy ◽  
Proposed Model ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
The Cost

Purpose This paper aims to obtain accurate forecasts of the hourly residential natural gas consumption, in Egypt, taken into consideration the volatile multiple seasonal nature of the gas series. This matter helps in both minimizing the cost of energy and maintaining the reliability of the Egyptian power system as well. Design/methodology/approach Double seasonal autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity model is used to obtain accurate forecasts of the hourly Egyptian gas consumption series. This model captures both daily and weekly seasonal patterns apparent in the series as well as the volatility of the series. Findings Using the mean absolute percentage error to check the forecasting accuracy of the model, it is proved that the produced outcomes are accurate. Therefore, the proposed model could be recommended for forecasting the Egyptian natural gas consumption. Originality/value The contribution of this research lies in the ingenuity of using time series models that accommodate both daily and weekly seasonal patterns, which have not been taken into consideration before, in addition to the series volatility to forecast hourly consumption of natural gas in Egypt.

Short-Term Forecasting of Natural Gas Consumption Using Factor Selection Algorithm and Optimized Support Vector Regression

2018 ◽  
Vol 141 (3) ◽  
Author(s):  
Nan Wei ◽  
Changjun Li ◽  
Chan Li ◽  
Hanyu Xie ◽  
Zhongwei Du ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Natural Gas ◽  
Support Vector Regression ◽  
Series Data ◽  
Support Vector ◽  
Selection Algorithm ◽  
Short Term ◽  
Proposed Model ◽  
Gas Consumption ◽  
Natural Gas Consumption

Forecasting of natural gas consumption has been essential for natural gas companies, customers, and governments. However, accurate forecasting of natural gas consumption is difficult, due to the cyclical change of the consumption and the complexity of the factors that influence the consumption. In this work, we constructed a hybrid artificial intelligence (AI) model to predict the short-term natural gas consumption and examine the effects of the factors in the consumption cycle. The proposed model combines factor selection algorithm (FSA), life genetic algorithm (LGA), and support vector regression (SVR), namely, as FSA-LGA-SVR. FSA is used to select factors automatically for different period based on correlation analysis. The LGA optimized SVR is utilized to provide the prediction of time series data. To avoid being trapped in local minima, the hyper-parameters of SVR are determined by LGA, which is enhanced due to newly added “learning” and “death” operations in conventional genetic algorithm. Additionally, in order to examine the effects of the factors in different period, we utilized the recent data of three big cities in Greece and divided the data into 12 subseries. The prediction results demonstrated that the proposed model can give a better performance of short-term natural gas consumption forecasting compared to the estimation value of existing models. Particularly, the mean absolute range normalized errors of the proposed model in Athens, Thessaloniki, and Larisa are 1.90%, 2.26%, and 2.12%, respectively.

scholarly journals Forecasting natural gas consumption of China by using a novel fractional grey model with time power term

2021 ◽  
Vol 7 ◽  
pp. 788-797
Author(s):  
Chong Liu ◽  
Wen-Ze Wu ◽  
Wanli Xie ◽  
Tao Zhang ◽  
Jun Zhang
Keyword(s):  
Natural Gas ◽  
Grey Model ◽  
Gas Consumption ◽  
Natural Gas Consumption

Application of Logistic Mathematical Model to Simulate the Consumption of Natural Gas in Brazil Until 2020

2012 ◽  
Author(s):  
Claudemir Duca Vasconcelos ◽  
Sérgio Ricardo Lourenço ◽  
Antonio Carlos Gracias ◽  
Valter Librais
Keyword(s):  
Mathematical Model ◽  
Population Dynamics ◽  
Natural Gas ◽  
Environmental Benefits ◽  
Linear Regression Method ◽  
Total Energy Consumption ◽  
Proposed Model ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Million Cubic Meter

Over the next two decades Brazil expects to see an increasing share of energy provided by natural gas. Natural gas use in 2010 was 10.2% of the total energy consumption and comprised 73 million cubic meter per day. Natural gas is considered one of the main energy sources which contributes and will contribute to the development of the country by providing economic, social and environmental benefits. This work uses population dynamics mathematical model (Verhulst) in order to analyze the evolution of natural gas consumption in Brazil within a forecast until 2020. The Verhulst model, also known as “logistic”, has been advanced in its application and shows that the population dynamics mathematical models, within their basic assumptions, may allow estimating the growth of a population or other variable with the same characteristic. The calculation to solve the differential equations, linear regression, method of least squares and graphics were performed using the MatLab software. Through simulations it was found that the mathematical model can be applied to natural gas consumption in Brazil. Simulations using the proposed model show a tendency of saturation in the consumption between 70 and 90 million cubic meters per day. These numbers are below the projected numbers until 2020 according to forecast presented in the energy plan. The results of this study indicate that the growth of natural gas consumption is stable and is within the limit of saturation related to the Brazilian market.

A Discussion on the Development of LNG Based on Market Concentration of Natural Gas Consumption

2013 ◽  
Vol 341-342 ◽  
pp. 1330-1333
Author(s):  
Ce Ming Zhang ◽  
Xiao Mei Huang ◽  
Shi Ni Peng ◽  
Chang Yin Liao
Keyword(s):  
Natural Gas ◽  
Energy Security ◽  
Hot Spot ◽  
Present Situation ◽  
Energy Market ◽  
Energy Structure ◽  
Efficient Energy ◽  
National Economic ◽  
Gas Consumption ◽  
Natural Gas Consumption

Liquefied natural gas (LNG) is a kind of clean and efficient energy. On one hand import LNG may diversify the energy structure and safeguard energy security for energy consumers, on the other hand the expansion in LNG export may well develop natural gas resources, increase foreign exchange income and promote national economic for energy producers. Therefore, LNG trade is becoming a new hot spot in the global energy market. In this paper, LNG consumption market structure was analyzed based on the popularization ratio of urban gas and consumption concentration of GDP worth of ten thousand yuan. LNG market present situation in China was explained accurately and reasonably.

scholarly journals Forecasting Natural Gas Consumption in the US Power Sector by a Randomly Optimized Fractional Grey System Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yubin Cai ◽  
Xin Ma ◽  
Wenqing Wu ◽  
Yanqiao Deng
Keyword(s):  
United States ◽  
Natural Gas ◽  
The United States ◽  
Grey Model ◽  
System Model ◽  
Grey System ◽  
Power Sector ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Grey System Model

Natural gas is one of the main energy resources for electricity generation. Reliable forecasting is vital to make sensible policies. A randomly optimized fractional grey system model is developed in this work to forecast the natural gas consumption in the power sector of the United States. The nonhomogeneous grey model with fractional-order accumulation is introduced along with discussions between other existing grey models. A random search optimization scheme is then introduced to optimize the nonlinear parameter of the grey model. And the complete forecasting scheme is built based on the rolling mechanism. The case study is executed based on the updated data set of natural gas consumption of the power sector in the United States. The comparison of results is analyzed from different step sizes, different grey system models, and benchmark models. They all show that the proposed method has significant advantages over the other existing methods, which indicates the proposed method has high potential in short-term forecasting for natural gas consumption of the power sector in United States.

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