Research on Prediction Model of Natural Gas Consumption Based on Grey Modeling Optimized by Genetic Algorithm

2009 ◽  
Author(s):  
Yan Xie ◽  
Mu Li
Keyword(s):  
Genetic Algorithm ◽  
Natural Gas ◽  
Prediction Model ◽  
Gas Consumption ◽  
Natural Gas Consumption

The application of trigonometric grey prediction model to average per capita natural gas consumption of households in China

2019 ◽  
Vol 9 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Qiuping Wang ◽  
Subing Liu ◽  
Haixia Yan
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Forecasting Model ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model ◽  
Grey Forecasting ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Per Capita

Purpose Due to high efficiency and low carbon of natural gas, the consumption of natural gas is increasing rapidly, and the prediction of natural gas consumption has become the focus. The purpose of this paper is to employ a prediction technique by combining grey prediction model and trigonometric residual modification for predicting average per capita natural gas consumption of households in China. Design/methodology/approach The GM(1,1) model is utilised to obtain the tendency term, then the generalised trigonometric model is used to catch the periodic phenomenon from the residual data of GM(1,1) model for improving predicting accuracy. Findings The case verified the view of Xie and Liu: “When the value of a is less, DGM model and GM(1,1) model can substitute each other.” The combination of the GM(1,1) and the trigonometric residual modification technique can observably improve the predicting accuracy of average per capita natural gas consumption of households in China. The mean absolute percentage errors of GM(1,1) model, DGM(1,1), unbiased grey forecasting model, and TGM model in ex post testing stage (from 2013 to 2015) are 32.5510, 33.5985, 36.9980, and 5.2996 per cent, respectively. The TGM model is suitable for the prediction of average per capita natural gas consumption of households in China. Practical implications According to the historical data of average per capita natural gas consumption of households in China, the authors construct GM(1,1) model, DGM(1,1) model, unbiased grey forecasting model, and GM(1,1) model with trigonometric residual modification. The accuracy of TGM is the best. TGM helps to improve the accuracy of GM(1,1). Originality/value This paper gives a successful practical application of grey model GM(1,1) with the trigonometric residual modification, where the cyclic variations exist in the residual series. The case demonstrates the effectiveness of trigonometric grey prediction model, which is helpful to understand the modeling mechanism of trigonometric grey prediction model.

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 China’s Natural Gas Consumption Based on AdaBoost-Particle Swarm Optimization-Extreme Learning Machine Integrated Learning Method

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2938 ◽  
Author(s):  
Gejirifu De ◽  
Wangfeng Gao
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Influencing Factors ◽  
Integrated Learning ◽  
Random Forest Algorithm ◽  
Swarm Optimization ◽  
The Core ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Learning Machine

With the orderly advancement of ‘China's energy development strategic action plan’, the natural gas industry has achieved unprecedented development. Currently, it is planned that by 2020, China’s natural gas consumption will account for at least 10% of the total primary energy consumption, have an orderly and improved energy structure, and achieved energy-saving and emission-reduction targets. Therefore, the accurate prediction of natural gas consumption becomes significantly important. Firstly, based on the research status of forecasting methods and the factors which affect natural gas consumption, this paper used the particle swarm optimization (PSO) algorithm to obtain the input layer weight, and used the optimized extreme learning machine (ELM) algorithm to obtain the hidden layer threshold; by using PSO-ELM as the base predictor and the AdaBoost algorithm, we have constructed the natural gas consumption integrated learning prediction model. Secondly, from the perspective of different provinces and industries, we deeply analyze the current status of natural gas consumption, and the random forest algorithm is used to extract the core influencing factors of natural gas consumption as the independent variables of the prediction model. Finally, data on China's natural gas consumption from 1995 to 2017 are selected, then the feasibility analysis and comparative analysis with other methods are performed. The results show: 1) Using the random forest algorithm to extract the core influencing factors, economic growth, population, household consumption and import dependence degree are significantly representative. 2) Based on the AdaBoost integrated learning algorithm, transforming the weak predictor with poor prediction effect into a strong predictor with strong prediction effect, compared with PSO-ELM、AdaBoost-ELM and ELM algorithm, with R-Square as 0.9999, Mean Square Error (MSE) as 0.8435，Mean Absolute Error (MAE) as 0.2379, Mean Absolute Percentage Error (MAPE) as 0.0008，effectively validated the significant effect of the AdaBoost-PSO-ELM prediction model. 3) Based on the AdaBoost-PSO-ELM prediction model, predict the natural gas core influencing factors and natural gas consumption in the year of 2018–2030. There is an apparent growth trend in the next 13 years, and the average growth rate of natural gas consumption has reached 7.68%.

Application of Genetic Algorithm (GA) Technique on Demand Estimation of Fossil Fuels in Turkey

2007 ◽  
Author(s):  
Olcay Ersel Canyurt ◽  
Harun Kemal O¨ztu¨rk
Keyword(s):  
Genetic Algorithm ◽  
Natural Gas ◽  
Fossil Fuels ◽  
Demand Estimation ◽  
Design Parameters ◽  
The Future ◽  
Oil And Natural Gas ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Natural Gas Demand

The main objective of the present study is to investigate Turkey’s fossil fuels demand, projection and supplies by giving the structure of the Turkish industry and Turkish economic conditions. This present study develops several scenarios to analyze fossil fuels; such as, coal, oil and natural gas consumption and make future projections based on Genetic Algorithm (GA) notion, and examines the effect of the design parameters on the fossil fuels utilization values. The models developed in the nonlinear form are applied to the coal, oil and natural gas demand of Turkey. Several Genetic Algorithm Demand Estimation Models (GA-DEM) are developed to estimate the future coal, oil and natural gas demand values based on population, Gross National Product (GNP), import, export figures. It may be concluded that the proposed models can be used as an alternative solution and estimation techniques for the future fossil fuel utilization values of any country. Oil is the most important fuel in Turkey, contributing 43% of the Total Primary Energy Supply (TPES), followed by coal (almost 30% of TPES) and natural gas (11.8%). In the study, coil, oil and natural gas consumption of Turkey are projected. Estimation shows that the coal, oil and natural gas consumption values may increase 2.82, 1.73 and 4.83 times from 2000 to 2020.

Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm

2021 ◽  
Vol 299 ◽  
pp. 117256
Author(s):  
Georgios I. Tsoumalis ◽  
Zafeirios N. Bampos ◽  
Georgios V. Chatzis ◽  
Pandelis N. Biskas ◽  
Stratos D. Keranidis
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Natural Gas ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Long Short Term Memory

EXPERIENCE, PROSPECTS AND PROBLEMS OF COOPERATION BETWEEN CHINA AND TURKMENISTAN IN THE FIELDOF NATURAL GAS

2019 ◽  
pp. 323-329
Author(s):  
Y. JIA
Keyword(s):  
Natural Gas ◽  
Central Asia ◽  
Oil And Gas ◽  
The European Union ◽  
Multilateral Cooperation ◽  
Industrial Chain ◽  
The Usa ◽  
Gas Consumption ◽  
Slow Progress ◽  
Natural Gas Consumption

Since 2007, the use of natural gas in China depends on the import, and with an increase in natural gas consumption, gas imports are also constantly growing. In 2018, Chinas natural gas imports approached 100 billion cubic meters, which is 70 times more than in 2006. In recent years, increasing attention has been paid to the use of natural gas in China. Turkmenistan is Chinas main source of pipeline gas imports, and China is Turkmenistans largest exporter of natural gas. In the framework of the traditional model of oil and gas cooperation, China and Turkmenistan are facing such problems as the uniform content of cooperation, lack of close ties in the field of multilateral cooperation and slow progress in the development of the entire industrial chain. Cooperation between China and Central Asia in the field of oil and gas is increasingly affecting the nerves of other countries, except the five countries of Central Asia, but including Russia, Afghanistan, Pakistan, India, Iran and other countries of the Middle East, Japan, South Korea, etc. and even the European Union and the USA. Despite the favorable trading environment for both parties, there are also problems in the domestic market of Turkmenistan and the risks of international competition.

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