Management and estimation of thermal comfort, carbon dioxide emission and economic growth by support vector machine

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@Article{Mladenovic:2016:RSER,
  author =       "Igor Mladenovic and Svetlana Sokolov-Mladenovic and 
                 Milos Milovancevic and Dusan Markovic and 
                 Nenad Simeunovic",
  title =        "Management and estimation of thermal comfort, carbon
                 dioxide emission and economic growth by support vector
                 machine",
  journal =      "Renewable and Sustainable Energy Reviews",
  volume =       "64",
  pages =        "466--476",
  year =         "2016",
  ISSN =         "1364-0321",
  DOI =          "doi:10.1016/j.rser.2016.06.034",
  URL =          "http://www.sciencedirect.com/science/article/pii/S136403211630257X",
  abstract =     "Urbanization and climate change are two defining
                 environmental phenomena and these two processes are
                 increasingly interconnected, as rapid urbanization is
                 often accompanied by a change in lifestyle, increasing
                 consumptions and energy uses, which contribute heavily
                 towards climate change and thermal comfort. Success of
                 public urban areas in attraction of residents depends
                 on thermal comfort of the visitors. Thermal comfort of
                 urban open spaces is variable, because it depends on
                 climatic parameters and other influences, which are
                 changeable throughout the year, as well as during the
                 day. Therefore, the prediction of thermal comfort is
                 significant in order to enable planning the time of
                 usage of urban open spaces. This paper presents Support
                 Vector Machine (SVM) to predict thermal comfort of
                 visitors at an open urban area. Results from SVM-FFA
                 were compared with two other soft computing method
                 namely artificial neural network (ANN) and genetic
                 programming (GP). The purpose of this research is also
                 to predict carbon dioxide (CO2) emission based on the
                 urban and rural population growth. Estimating carbon
                 dioxide (CO2) emissions at an urban scale is the first
                 step for adaptation and mitigation of climate change by
                 local governments. The environment that governs the
                 relationships between carbon dioxide (CO2) emissions
                 and gross domestic product (GDP) changes over time due
                 to variations in economic growth, regulatory policy and
                 technology. The relationship between economic growth
                 and carbon dioxide emissions is considered as one of
                 the most important empirical relationships. GDP is also
                 predicted based on CO2 emissions. The reliability of
                 the computational models were accessed based on
                 simulation results and using several statistical
                 indicators.",
  keywords =     "genetic algorithms, genetic programming, Thermal
                 comfort, Economic growth, Carbon dioxide emission,
                 Support vector machine",
}

Genetic Programming entries for Igor Mladenovic Svetlana Sokolov-Mladenovic Milos Milovancevic Dusan Markovic Nenad Simeunovic

Citations