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.

The future of natural gas consumption in Beijing, Guangdong and Shanghai: An assessment utilizing MARKAL

Energy Policy ◽  
2008 ◽  
Vol 36 (9) ◽  
pp. 3286-3299 ◽  
Author(s):  
BinBin Jiang ◽  
Chen Wenying ◽  
Yu Yuefeng ◽  
Zeng Lemin ◽  
David Victor
Keyword(s):  
Natural Gas ◽  
The Future ◽  
Gas Consumption ◽  
Natural Gas Consumption

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.

Introduction to this special section: Exploration geophysics in China

2019 ◽  
Vol 38 (8) ◽  
pp. 596-596
Author(s):  
Yongyi Li ◽  
Xiaogui Miao ◽  
Shoudong Huo ◽  
Jianwei Ma ◽  
Danping Cao
Keyword(s):  
Natural Gas ◽  
Oil And Gas ◽  
Special Section ◽  
Petroleum Industry ◽  
Gas Production ◽  
Oil Consumption ◽  
Oil And Gas Production ◽  
Oil And Natural Gas ◽  
Gas Consumption ◽  
Natural Gas Consumption

China ranks second and third in global oil and natural gas consumption, and fifth and sixth in global oil and natural gas production, respectively ( U.S. EIA, 2018 ). In the past 25 years, China's oil consumption has increased 3.5 times, and natural gas consumption is rising rapidly as well. China is increasing its investment in the petroleum industry, with a goal of significantly expanding domestic oil and gas production. Complex geology, rough surface conditions, and the need to explore deep targets, unconventional resources, and offshore reservoirs pose great challenges to geophysical exploration. Geophysical technologies in China thus have advanced significantly in data acquisition, processing, and interpretation. To demonstrate the development and applications of geophysical technologies in the exploration, development, and production of oil and gas resources, we invited academic and industry experts to present recent studies on exploration geophysics in China.

scholarly journals Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2317 ◽  
Author(s):  
Konstantinos Papageorgiou ◽  
Elpiniki I. Papageorgiou ◽  
Katarzyna Poczeta ◽  
Dionysis Bochtis ◽  
George Stamoulis
Keyword(s):  
Neural Networks ◽  
Natural Gas ◽  
Fuzzy Inference ◽  
Inference System ◽  
Hybrid Combination ◽  
Neuro Fuzzy ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Natural Gas Demand ◽  
Consumption Demand

(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate natural gas supply planning and apply the right strategic policies in this direction. In order to develop a real accurate natural gas (NG) prediction model for Greece, we examine the application of neuro-fuzzy models, which have recently shown significant contribution in the energy domain. (2) Methods: The adaptive neuro-fuzzy inference system (ANFIS) is a flexible and easy to use modeling method in the area of soft computing, integrating both neural networks and fuzzy logic principles. The present study aims to develop a proper ANFIS architecture for time series modeling and prediction of day-ahead natural gas demand. (3) Results: An efficient and fast ANFIS architecture is built based on neuro-fuzzy exploration performance for energy demand prediction using historical data of natural gas consumption, achieving a high prediction accuracy. The best performing ANFIS method is also compared with other well-known artificial neural networks (ANNs), soft computing methods such as fuzzy cognitive map (FCM) and their hybrid combination architectures for natural gas prediction, reported in the literature, to further assess its prediction performance. The conducted analysis reveals that the mean absolute percentage error (MAPE) of the proposed ANFIS architecture results is less than 20% in almost all the examined Greek cities, outperforming ANNs, FCMs and their hybrid combination; and (4) Conclusions: The produced results reveal an improved prediction efficacy of the proposed ANFIS-based approach for the examined natural gas case study in Greece, thus providing a fast and efficient tool for utterly accurate predictions of future short-term natural gas demand.

Price and Income Elasticities of Natural Gas Demand in Egypt: A Bound Test Approach

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Markos Farag ◽  
Chahir Zaki
Keyword(s):  
Natural Gas ◽  
Economic Activity ◽  
Income Elasticities ◽  
Price And Income Elasticities ◽  
Long Run ◽  
Short Run ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Natural Gas Demand ◽  
Income And Price Elasticities

Abstract This paper provides short and long-run estimates of price and income elasticities of Egypt’s natural gas demand using the ARDL bounds testing approach to cointegration over the period 1983–2015. The results show that the long-run income and price elasticities, in absolute values, are greater than their counterparts in the short run. This result is due to the fact that consumers can modify their consumption habits and plans in the long run as a response to changes in the income or the price. Moreover, natural gas demand is more responsive to changes in income than changes in price in both the short and long run. Finally, the study examines the causality relationship between natural gas consumption and economic growth for the gas-consuming sectors in Egypt. The results indicate that there is no causal relationship between the two variables for the electricity, petroleum, and household sectors in the short-run. By contrast, there is a unidirectional causality running from natural gas consumption to the economic activity of the transportation sector and a unidirectional causality running from economic activity to natural gas consumption by the industry sector.

scholarly journals Probing the Energy-Environmental Kuznets Curve Hypothesis in Oil and Natural Gas Consumption Models Considering Urbanization and Financial Development in Middle East Countries

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3178
Author(s):  
Haider Mahmood ◽  
Nabil Maalel ◽  
Muhammad Shahid Hassan
Keyword(s):  
Saudi Arabia ◽  
Natural Gas ◽  
Oil Consumption ◽  
Long Run ◽  
Negative Effect ◽  
Short Run ◽  
Oil And Natural Gas ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Positive Effect

Economic growth, urbanization, and financial market development (FMD) may increase energy demand in any economy. Non-renewable sources of energy consumption, i.e., oil consumption and natural gas consumption (NGC), could have environmental consequences. We examine the effects of economic growth, urbanization, and FMD on the oil consumption and NGC in Middle East countries using the period 1975–2019. In the panel results, we found a positive effect of income and a negative effect of income-squared on oil and natural gas consumption. Hence, we corroborate the existence of the environmental Kuznets curve (EKC) hypothesis in oil and natural gas consumption models of the Middle East region. Urbanization has a positive effect on oil and natural gas consumption. FMD has a positive effect on oil consumption and has a negative effect on NGC. From the long-run, country-specific results, we validate the existence of the EKC hypothesis in the oil consumption models of Iran and Iraq. The EKC is also found in the natural gas consumption models of Iran, Kuwait, and the UAE. From the short-run results, the EKC hypothesis is validated in the oil consumption models of Iran, Iraq, and Israel. The EKC is also corroborated in the NGC models of Iran, Kuwait, and the UAE. In the long run, urbanization has a positive effect on oil consumption in Iraq, Kuwait, Saudi Arabia, and Qatar. Further, urbanization has a positive effect on the NGC in Iraq, Israel, and Saudi Arabia. Conversely, urbanization has a negative effect on oil consumption in Israel. In the short run, urbanization has a positive effect on oil consumption in Iraq, Israel, Kuwait, and Qatar. Moreover, urbanization has a positive effect on the NGC in Iraq. On the other hand, urbanization has a negative effect on oil consumption in Saudi Arabia and Iran. In the long run, FMD has a positive effect on oil consumption in Saudi Arabia and Israel. In the short run, FMD has a positive effect on oil consumption in Israel, Kuwait, and Saudi Arabia. In contrast, FMD has a negative effect on oil consumption in the UAE. Moreover, a positive effect of FMD on NGC is found in the UAE. However, FMD has a negative effect on the NGC in Israel.

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
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