2020
DOI: 10.1002/ese3.593
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Technological assessment and modeling of energy‐related CO2 emissions for the G8 countries by using hybrid IWO algorithm based on SVM

Abstract: Recently, energy‐related CO2 emissions are considered as one of the most crucial issues and are promptly augmented due to further urbanization. In this paper, in order to model and calculate yearly CO2 emission, an artificial neural network is used. For the first time, the IWO‐SVM method has been applied in modeling energy‐related CO2 emissions. In this regard, consumption of different energy sources such as renewable energy, natural gas, coal, and oil, and GDP of the G8 countries in various years (from 1990 t… Show more

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Cited by 25 publications
(5 citation statements)
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“…Moreover, the studies are presented in Refs. [ 20 , 21 ] on group method data handling to mitigate CO2 emissions by modelling various energy systems of G8 countries. A comparative study based on different study periods, variables, and methodological applications was conducted in Refs.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Moreover, the studies are presented in Refs. [ 20 , 21 ] on group method data handling to mitigate CO2 emissions by modelling various energy systems of G8 countries. A comparative study based on different study periods, variables, and methodological applications was conducted in Refs.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Ahmadi et al [14] used GMDH to estimate the CO 2 emission of some countries in the Middle East by considering energyrelated factors and GDP and reached R 2 of 0.9998. In another research, Ghazvini et al [15] applied an intelligent technique based on support vector machine to model CO 2 emission in G8 countries. They found that using an optimization algorithm to minimize the error affects the exactness of the predicted values.…”
Section: Introductionmentioning
confidence: 99%
“…Household consumption: in 2018, Iranian household electricity consumption (mainly including lighting, home appliances, and cooling devices) was ∼29,600 kWh per capita, showing a 3.0% reduction compared with 2017. Commercial consumption: It averaged ∼4142.7 kWh (7.3%), showing a 0.9% reduction compared with 2017 3–11 …”
Section: Introductionmentioning
confidence: 99%
“…Commercial consumption: It averaged ∼4142.7 kWh (7.3%), showing a 0.9% reduction compared with 2017. [3][4][5][6][7][8][9][10][11] Public sector consumption: This represents 9.3% of the electricity sale of the Ministry of Energy in 2018, with an average consumption of ∼14,449.7 kWh per customer (decreased by 3.4% compared with 2017). Industrial consumption: 34.1% of the electricity sale of the Ministry of Energy in 2018 was for industries (88.5 TWh excluding transportation, increased by 1.5% compared with 2017).…”
Section: Introductionmentioning
confidence: 99%