# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 3 Solar Site Evaluation Using GISs  
### Overview  
As regulations for environmental pollution are becoming stricter, there is a growing need to install generating plants using solar energy. To increase the effects of installing such plants, it is necessary to prioritize and select suitable locations to maximize the electricity generation and to minimize the damage that may occur. The results of this site analysis can help solar utility companies, energy companies, and policy makers select potential sites for the construction of solar power plants. Solar power generating plants are heavily influenced by solar radiation, and the distribution of solar resources varies considerably by location. In most studies in which GISs have been used for site selection, this solar radiation has been considered as an input layer. Depending on the type of solar power plant, the type of solar radiation used for analysis can be distinguished. Solar power plants can be divided into PV plants and CSP plants. Unlike PV plants where diffuse radiation is also important, direct radiation plays an important role in CSP plants. Therefore, GHI is mainly used for studies on PV plants, and DNI is mainly used for studies on CSP plants. In some site evaluation studies, only general solar energy distributions have been analyzed, without classifying PV and CSP plants. The types of power plants are summarized in Table 2 based on the reviewed literature. GIS-based methods of evaluating suitable locations for solar power plants can be largely classified depending on whether they are applied on a regional or local scale. Site selection studies using GISs have been widely conducted on the regional scale, and the results have been usefully applied for policy and installation planning. When performing site evaluation on the regional scale, it is necessary to consider various factors such as economic, environmental, technical, social, and risk factors related to the installation of solar power plants, as well as solar radiation. For example, it is difficult to install large-scale solar power plants in the center of cities, so land availability must be considered. In addition, solar power plants must be accessible for installation, so it is necessary to consider the distance to the road network. Solar power plants also should not be far from the power grid to transmit the electricity produced. Many other factors must be considered, and some of these factors serve as constraints to exclude areas where solar power plants should not be installed or as suitability criteria to quantify suitability. Even if the same GIS layer is used, it can be employed in different ways depending on the research. GIS-based multi-criteria analysis basically relies on two main approaches: Boolean overlay operators and weighted summations procedures. Although there are some differences in the definition of the term multi-criteria decision analysis (MCDA) according to the literature, MCDA is specified as a method of quantifying suitability using weights (i.e., weighted summation procedures) in this paper. The term multi-criteria decision making (MCDM) is also used instead of MCDA in some literature. The Boolean overlay simply determines whether any conditions are satisfied, and analyzes the suitable area that satisfies all conditions. These results can be used as constraints in weighted summation analysis methods. Weighted summation can be subdivided according to the method of weighting each factor. In this study, we use the term weighted sum when we use the same weight or a very simple weight. The Analytic Hierarchy Process (AHP) method is the most widely used method to quantify the weight according to the expert opinion. Fuzzy AHP (FAHP), which combines fuzzy theory with AHP, is used to mitigate the uncertainty that may arise in this process. A typical flow chart of these GIS-based MCDA methods is shown in Figure 2.