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Integrated intelligent geoprocessing tool for screening candidate locations suitable for Distributed Generation Deployment

Author

Listed:
  • Marcochi de Melo, Diego
  • Lopes, Elaine Coelho
  • Motta, Juliana Gutierrez
  • Asano, Roberto
  • Valverde, María
  • Suyama, Ricardo
  • Benedito, Ricardo da Silva
  • Leite, Patricia Teixeira
  • Cardoso de Lima, Gracieli Sartório

Abstract

The pursuit of economic growth leads to an increase demand for energy that, in turn, may cause negative impacts on the environment due to consumption of non-renewable resources. Thus, it is necessary to search for sustainable strategies for the electric energy's conversion, standing out the distributed generation and the renewable energy sources. In this context, this work presents an intelligent computational tool that combines the concepts of sustainable development and distributed generation to detect locations that are suitable for the installation of renewable energy sources such as wind turbines and solar energy systems. This tool incorporates an intelligent system based on fuzzy logic integrated within a geoprocessing software package, providing an indicative methodology to assist the stakeholders of the energy industry in their studies of energetic planning aiming a sustainable development.

Suggested Citation

  • Marcochi de Melo, Diego & Lopes, Elaine Coelho & Motta, Juliana Gutierrez & Asano, Roberto & Valverde, María & Suyama, Ricardo & Benedito, Ricardo da Silva & Leite, Patricia Teixeira & Cardoso de Lima, 2021. "Integrated intelligent geoprocessing tool for screening candidate locations suitable for Distributed Generation Deployment," Renewable Energy, Elsevier, vol. 177(C), pages 797-806.
    • Handle: RePEc:eee:renene:v:177:y:2021:i:c:p:797-806
    DOI: 10.1016/j.renene.2021.05.100
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    References listed on IDEAS

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    1. Azadeh, A. & Sheikhalishahi, M. & Asadzadeh, S.M., 2011. "A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity," Renewable Energy, Elsevier, vol. 36(12), pages 3394-3401.
    2. Cardoso de Lima, Gracieli Sartório & Lopes, Elaine Coelho & Motta, Juliana Gutierrez & Asano, Roberto & Valverde, María & Suyama, Ricardo & Leite, Patricia Teixeira, 2018. "Sustainable development enhanced in the decision process of electricity generation expansion planning," Renewable Energy, Elsevier, vol. 123(C), pages 563-577.
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    5. Tom Waas & Jean Huge & Thomas BLOCK & Tarah Wright & Francisco Javier Benitez Capistros & Aviel Verbruggen, 2014. "Sustainability assessment and indicators: Tools in a decision-making strategy for sustainable development," ULB Institutional Repository 2013/189410, ULB -- Universite Libre de Bruxelles.
    6. Gigović, Ljubomir & Pamučar, Dragan & Božanić, Darko & Ljubojević, Srđan, 2017. "Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia," Renewable Energy, Elsevier, vol. 103(C), pages 501-521.
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