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Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique

Author

Listed:
  • Konstantinos Papageorgiou

    (Department of Computer Science, University of Thessaly, Lamia 35100, Greece)

  • Gustavo Carvalho

    (Faculty of Economics, Business Administration and Accounting, University of Sao Paulo (USP), São Paulo 05508-010, Brazil)

  • Elpiniki I. Papageorgiou

    (Faculty of Technology, University of Thessaly, Geopolis Campus, Larissa 41500, Greece
    Institute for Bio-economy and Agri-technology (iBO), Center for Research and Technology – Hellas (CERTH), Thessaloniki 57001, Greece)

  • Dionysis Bochtis

    (Institute for Bio-economy and Agri-technology (iBO), Center for Research and Technology – Hellas (CERTH), Thessaloniki 57001, Greece)

  • George Stamoulis

    (Department of Computer Science, University of Thessaly, Lamia 35100, Greece)

Abstract

Photovoltaic Solar Energy (PSE) sector has sparked great interest from governments over the last decade towards diminution of world’s dependency to fossil fuels, greenhouse gas emissions reduction and global warming mitigation. Photovoltaic solar energy also holds a significant role in the transition to sustainable energy systems. These systems and their optimal exploitation require an effective supply chain management system, such as design of the network, collection, storage, or transportation of this energy resource, without disregarding a country’s certain socio-economic and political conditions. In Brazil, the adoption of photovoltaic solar energy has been motivated not only by the energy matrix diversification but also from the shortages, problems, and barriers that the Brazilian energy sector has faced, lately. However, PSE development is affected by various factors with high uncertainty, such as political, social, economic, and environmental, that include critical operational sustainability issues. Thus, an elaborate modelling of energy management and a well-structured decision support process are needed to enhance the performance efficiency of Brazilian PSE supply chain management. This study focuses on the investigation of certain factors and their influence on the development of the Brazilian PSE with the help of Fuzzy Cognitive Maps. Fuzzy Cognitive Map is an established methodology for scenario analysis and management in diverse domains, inheriting the advancements of fuzzy logic and neural networks. In this context, a semi-quantitative model was designed with the help of various stakeholders from the specific energy domain and three plausible scenarios were conducted in order to support a decision-making process on PSE sector development and the country’s economic potential. The outcome of this analysis reveals that the development of the PSE sector in Brazil is mainly affected by economic and political factors.

Suggested Citation

  • Konstantinos Papageorgiou & Gustavo Carvalho & Elpiniki I. Papageorgiou & Dionysis Bochtis & George Stamoulis, 2020. "Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique," Energies, MDPI, vol. 13(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1427-:d:334223
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    References listed on IDEAS

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    Cited by:

    1. Maria A. Franco & Stefan N. Groesser, 2021. "A Systematic Literature Review of the Solar Photovoltaic Value Chain for a Circular Economy," Sustainability, MDPI, vol. 13(17), pages 1-35, August.
    2. Athanasios Tsadiras & Maria Pempetzoglou & Iosif Viktoratos, 2021. "Making Predictions of Global Warming Impacts Using a Semantic Web Tool that Simulates Fuzzy Cognitive Maps," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 715-745, October.
    3. Becerra-Fernandez, Mauricio & Sarmiento, Alfonso T. & Cardenas, Laura M., 2023. "Sustainability assessment of the solar energy supply chain in Colombia," Energy, Elsevier, vol. 282(C).
    4. Katarzyna Poczeta & Elpiniki I. Papageorgiou & Vassilis C. Gerogiannis, 2020. "Fuzzy Cognitive Maps Optimization for Decision Making and Prediction," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
    5. Charisios Achillas & Dionysis Bochtis, 2021. "Supply Chain Management for Bioenergy and Bioresources: Bridging the Gap between Theory and Practice," Energies, MDPI, vol. 14(19), pages 1-4, September.
    6. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    7. Ghaboulian Zare, Sara & Alipour, Mohammad & Hafezi, Mehdi & Stewart, Rodney A. & Rahman, Anisur, 2022. "Examining wind energy deployment pathways in complex macro-economic and political settings using a fuzzy cognitive map-based method," Energy, Elsevier, vol. 238(PA).
    8. Olga Pilipczuk, 2020. "Sustainable Smart Cities and Energy Management: The Labor Market Perspective," Energies, MDPI, vol. 13(22), pages 1-24, November.

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