文章基本信息

标题：Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN
本地全文：下载
作者：M. Yousefi ; M. Omid ; Sh. Rafiee 等
期刊名称：International Journal of Energy and Environment
印刷版ISSN：2076-2895
电子版ISSN：2076-2909
出版年度：2013
卷号：4
期号：6
页码：1041-1052
出版社：International Energy and Environment Foundation (IEEF)
摘要：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.