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A decision model based on decision tree and particle swarm optimization algorithms to identify optimal locations for solar power plants construction in Iran

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  • Shorabeh, Saman Nadizadeh
  • Samany, Najmeh Neysani
  • Minaei, Foad
  • Firozjaei, Hamzeh Karimi
  • Homaee, Mehdi
  • Boloorani, Ali Darvishi

Abstract

Sustainable energy sources such as solar energy are the most common renewable alternatives to fossil fuels. The first step in expanding the use of solar energy is to identify high-potential locations for the construction of solar power plants. This study aims to assess and compare the performance of decision tree and particle swarm optimization (PSO) algorithms in identifying optimal solar power locations. High-potential regions are extracted using multiple-criteria decision making (MCDM), which are used in the decision tree and PSO algorithms for recognizing optimal locations. The results indicate the potential of the study area for establishing solar power plants based on three-level of suitability. The comparison of optimization methods showed that the prediction rate for the very high potential class belonged to the decision tree (0.29) and PSO (0.13), respectively. Therefore, the decision tree method offers a better solution for detecting potential areas for solar energy development in the study area. The research results provide a useful resource for planning and decision-making concerning the development of renewable energy in the future. The used decision tree in the present study can also be used in other parts of the world to select optimal locations for constructing renewable energy plants.

Suggested Citation

  • Shorabeh, Saman Nadizadeh & Samany, Najmeh Neysani & Minaei, Foad & Firozjaei, Hamzeh Karimi & Homaee, Mehdi & Boloorani, Ali Darvishi, 2022. "A decision model based on decision tree and particle swarm optimization algorithms to identify optimal locations for solar power plants construction in Iran," Renewable Energy, Elsevier, vol. 187(C), pages 56-67.
    • Handle: RePEc:eee:renene:v:187:y:2022:i:c:p:56-67
    DOI: 10.1016/j.renene.2022.01.011
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    3. Gao, Mingfei & Han, Zhonghe & Zhang, Ce & Li, Peng & Wu, Di & Li, Peng, 2023. "Optimal configuration for regional integrated energy systems with multi-element hybrid energy storage," Energy, Elsevier, vol. 277(C).
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    6. Bahare Moradi & Rojin Akbari & Seyedeh Reyhaneh Taghavi & Farnaz Fardad & Abdulsalam Esmailzadeh & Mohammad Zia Ahmadi & Sina Attarroshan & Fatemeh Nickravesh & Jamal Jokar Arsanjani & Mehdi Amirkhani, 2023. "A Scenario-Based Spatial Multi-Criteria Decision-Making System for Urban Environment Quality Assessment: Case Study of Tehran," Land, MDPI, vol. 12(9), pages 1-24, August.
    7. Davoud Shahpari Sani & Mohammad Taghi Heidari & Hossein Tahmasebi Mogaddam & Saman Nadizadeh Shorabeh & Saman Yousefvand & Anahita Karmpour & Jamal Jokar Arsanjani, 2022. "An Assessment of Social Resilience against Natural Hazards through Multi-Criteria Decision Making in Geographical Setting: A Case Study of Sarpol-e Zahab, Iran," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    8. Nimmanterdwong, Prathana & Chalermsinsuwan, Benjapon & Piumsomboon, Pornpote, 2023. "Optimizing utilization pathways for biomass to chemicals and energy by integrating emergy analysis and particle swarm optimization (PSO)," Renewable Energy, Elsevier, vol. 202(C), pages 1448-1459.
    9. Mohammad Ebrahimi Sirizi & Esmaeil Taghavi Zirvani & Abdulsalam Esmailzadeh & Jafar Khosravian & Reyhaneh Ahmadi & Naeim Mijani & Reyhaneh Soltannia & Jamal Jokar Arsanjani, 2023. "A Scenario-Based Multi-Criteria Decision-Making Approach for Allocation of Pistachio Processing Facilities: A Case Study of Zarand, Iran," Sustainability, MDPI, vol. 15(20), pages 1-21, October.

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