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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. • A new approach to identify optimal locations for solar power plants in Iran. • The decision tree and PSO are used for the first time in solar energy assessment. • Raster and vector spatial criteria are considered to assess site feasibility. • The results show the efficiency of the decision tree for finding solar power plants. [ABSTRACT FROM AUTHOR]

Identifying potential locations for installation of solar power plants is a critical step in utilizing sustainable energy resources. In this study, a GIS-based Multi-Criteria Decision Analysis (GIS-MCDA) technique is used to generate maps that represent potential areas for solar power plants in four provinces with different climate conditions in Iran. The concept of risk is included in the GIS-MCDA process using the Ordered Weighted Averaging (OWA) model. The OWA model can provide various risk-taking (optimistic) and risk-aversion (pessimistic) scenarios to determine the suitable power plant areas. The results of this study indicate that provinces located in an arid climate such as Yazd contain a more suitable area for the solar power plants compared to wet climate provinces (e.g., Mazandaran). The sensitivity analysis of results show that the criterion "fault" has the minimum effect while the criteria "slope" and "road network" have the maximum effects on the area of the highly desirable class. • The GIS-MCDA is used to evaluate the locations for establishment of solar power plants. • The evaluation process involves the locations in different climate conditions. • An OWA-based technique is utilized to incorporate risk in solar energy assessment. • The sensitivity of results to each criterion is evaluated. [ABSTRACT FROM AUTHOR]

Abstract Solar energy is one of the important energy sources and countries have realized the important role of renewable energies due to the depletion of conventional energy sources. In this study, a GIS-based analysis is utilized for investigating the feasibility of solar energy in Iran. To evaluate the concept of risk into the GIS-based analysis for determining optimal areas for installation of solar power plants an Ordered Weighted Averaging (OWA) approach is used for the first time. Integration of OWA-based approach and GIS analysis provide models that determine the priority of regions from risk-free decision to risky decision strategies. The results show that Kerman, Yazd, Fars, Khuzestan, Sistan and Baluchistan, South Khorasan and Isfahan provinces have a good capacity to invest in solar energy projects. The GIS-based analysis indicates that the values of installed solar power plants percentages in high chosen areas for the most pessimistic and optimistic strategies are 7% and 64%, respectively. Highlights • A new model is used to evaluate the solar energy in Iran. • To evaluate solar energy potential a risk analysis is added to GIS-based method. • An Order Weight Averaging is utilized for first time in solar energy assessment. • A location analysis is done for currently working solar plants. • A maximum of 498 W/m2 solar radiation is achievable yearly. [ABSTRACT FROM AUTHOR]