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Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN

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.
Authors:
Yousefi, M.; Omid, M.; Rafiee, Sh.; [1]  Ghaderi, S. F. [2] 
  1. Department of Agricultural Machinery Engineering, University of Tehran, Karaj (Iran, Islamic Republic of)
  2. Department of Industrial Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)
Publication Date:
Jul 01, 2013
Product Type:
Journal Article
Resource Relation:
Journal Name: International Journal of Energy and Environment (Print); Journal Volume: 4; Journal Issue: 6
Subject:
29 ENERGY PLANNING, POLICY AND ECONOMY; LINEAR PROGRAMMING; NEURAL NETWORKS; CARBON DIOXIDE; EMISSION; ENERGY DEMAND
OSTI ID:
22188320
Country of Origin:
Iraq
Language:
English
Other Identifying Numbers:
Journal ID: ISSN 2076-2895; TRN: IQ14OA028
Availability:
Available from: http://www.ijee.ieefoundation.org/vol4/issue6/IJEE_11_v4n6.pdf
Submitting Site:
DEV
Size:
page(s) 1041-1052
Announcement Date:
Feb 06, 2014

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}
}