Management and estimation of thermal comfort, carbon dioxide emission and economic growth by support vector machine
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- @Article{Mladenovic:2016:RSER,
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author = "Igor Mladenovic and Svetlana Sokolov-Mladenovic and
Milos Milovancevic and Dusan Markovic and
Nenad Simeunovic",
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title = "Management and estimation of thermal comfort, carbon
dioxide emission and economic growth by support vector
machine",
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journal = "Renewable and Sustainable Energy Reviews",
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volume = "64",
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pages = "466--476",
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year = "2016",
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ISSN = "1364-0321",
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DOI = "doi:10.1016/j.rser.2016.06.034",
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URL = "http://www.sciencedirect.com/science/article/pii/S136403211630257X",
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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.",
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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