Volume 8, Issue 4 (1-2019)                   JEM 2019, 8(4): 38-49 | Back to browse issues page

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Hafezi R, Akhavan A, Pakseresht S. Development a Hybrid Computational Model Based on Artificial Neural Network to Predict Natural Gas Global Demand . JEM. 2019; 8 (4) :38-49
URL: http://energy.kashanu.ac.ir/article-1-961-en.html
Recently natural gas global market has attracted much attention since natural gas is much cleaner than oil and is also cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological developments, unconventional resources, energy security challenges, and shipment are some of the factors which have made energy market more dynamic and complex in the last decade. Studying the demand side behavior of natural gas market has been targeted by this research to focus on the plausible trends of global natural gas demands. This paper proposes a hybrid time series model which starts with data mining oriented techniques to detect input features and pre-processing data; then a neural network based prediction model is used to uncover global natural gas trends. Thirteen different features were studied, Yet six features were finally selected as the most relevant features: Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption. In the end, the proposed prediction model overcame other competitive models in regard to five different error-based evaluation statistics.
Type of Study: Research | Subject: Special

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