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

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  • Mladenović, Igor
  • Sokolov-Mladenović, Svetlana
  • Milovančević, Milos
  • Marković, Dušan
  • Simeunović, Nenad

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.

Suggested Citation

  • Mladenović, Igor & Sokolov-Mladenović, Svetlana & Milovančević, Milos & Marković, Dušan & Simeunović, Nenad, 2016. "Management and estimation of thermal comfort, carbon dioxide emission and economic growth by support vector machine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 466-476.
    • Handle: RePEc:eee:rensus:v:64:y:2016:i:c:p:466-476
    DOI: 10.1016/j.rser.2016.06.034
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    3. Yuhong Zhao & Ruirui Liu & Zhansheng Liu & Liang Liu & Jingjing Wang & Wenxiang Liu, 2023. "A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
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    5. Liu, Xiaoping & Ou, Jinpei & Chen, Yimin & Wang, Shaojian & Li, Xia & Jiao, Limin & Liu, Yaolin, 2019. "Scenario simulation of urban energy-related CO2 emissions by coupling the socioeconomic factors and spatial structures," Applied Energy, Elsevier, vol. 238(C), pages 1163-1178.

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