Abstract
Iran's primary energy consumption (PEC) was modeled as a linear function of five socioeconomic and meteorological explanatory variables using particle swarm optimization (PSO) and artificial neural networks (ANNs) techniques. Results revealed that ANN outperforms PSO model to predict test data. However, PSO technique is simple and provided us with a closed form expression to forecast PEC. Energy demand was forecasted by PSO and ANN using represented scenario. Finally, adapting about 10% renewable energy revealed that based on the developed linear programming (LP) model under minimum CO2 emissions, Iran will emit about 2520 million metric tons CO2 in 2025. The LP model indicated that maximum possible development of hydropower, geothermal and wind energy resources will satisfy the aim of minimization of CO2 emissions. Therefore, the main strategic policy in order to reduce CO2 emissions would be exploitation of these resources.
Yousefi, M.;
Omid, M.;
Rafiee, Sh.;
[1]
Ghaderi, S. F.
[2]
- Department of Agricultural Machinery Engineering, University of Tehran, Karaj (Iran, Islamic Republic of)
- Department of Industrial Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)
Citation Formats
Yousefi, M., Omid, M., Rafiee, Sh., and Ghaderi, S. F.
Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN.
Iraq: N. p.,
2013.
Web.
Yousefi, M., Omid, M., Rafiee, Sh., & Ghaderi, S. F.
Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN.
Iraq.
Yousefi, M., Omid, M., Rafiee, Sh., and Ghaderi, S. F.
2013.
"Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN."
Iraq.
@misc{etde_22188320,
title = {Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN}
author = {Yousefi, M., Omid, M., Rafiee, Sh., and Ghaderi, S. F.}
abstractNote = {Iran's primary energy consumption (PEC) was modeled as a linear function of five socioeconomic and meteorological explanatory variables using particle swarm optimization (PSO) and artificial neural networks (ANNs) techniques. Results revealed that ANN outperforms PSO model to predict test data. However, PSO technique is simple and provided us with a closed form expression to forecast PEC. Energy demand was forecasted by PSO and ANN using represented scenario. Finally, adapting about 10% renewable energy revealed that based on the developed linear programming (LP) model under minimum CO2 emissions, Iran will emit about 2520 million metric tons CO2 in 2025. The LP model indicated that maximum possible development of hydropower, geothermal and wind energy resources will satisfy the aim of minimization of CO2 emissions. Therefore, the main strategic policy in order to reduce CO2 emissions would be exploitation of these resources.}
journal = {International Journal of Energy and Environment (Print)}
issue = {6}
volume = {4}
journal type = {AC}
place = {Iraq}
year = {2013}
month = {Jul}
}
title = {Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN}
author = {Yousefi, M., Omid, M., Rafiee, Sh., and Ghaderi, S. F.}
abstractNote = {Iran's primary energy consumption (PEC) was modeled as a linear function of five socioeconomic and meteorological explanatory variables using particle swarm optimization (PSO) and artificial neural networks (ANNs) techniques. Results revealed that ANN outperforms PSO model to predict test data. However, PSO technique is simple and provided us with a closed form expression to forecast PEC. Energy demand was forecasted by PSO and ANN using represented scenario. Finally, adapting about 10% renewable energy revealed that based on the developed linear programming (LP) model under minimum CO2 emissions, Iran will emit about 2520 million metric tons CO2 in 2025. The LP model indicated that maximum possible development of hydropower, geothermal and wind energy resources will satisfy the aim of minimization of CO2 emissions. Therefore, the main strategic policy in order to reduce CO2 emissions would be exploitation of these resources.}
journal = {International Journal of Energy and Environment (Print)}
issue = {6}
volume = {4}
journal type = {AC}
place = {Iraq}
year = {2013}
month = {Jul}
}