A fuzzy multi-criteria spatial decision support system for solar farm location planning

In recent years, investment in solar energy has increased substantially across countries. Thus, selecting convenient locations for solar farms has become a fundamental problem when determining the investment required due to differences in climatic factors, the type and availability of land, transportation infrastructures, and the quality of power lines. Multi-Criteria Evaluation approaches based on crisp data are generally used in the selection process of optimal locations. However, despite being crisp, the data available when considering the evaluation criteria of the different alternatives constitute a discrete approximation performed on a spatial grid of potential locations. Thus, we introduce a three-stage fuzzy evaluation framework designed to account for the imprecision inherent to the evaluations when identifying the most convenient location for constructing solar power farms. First, we implement ANFIS (Adaptive Neuro-Fuzzy Inference System) on the set of grid intersection crisp data points and derive a coherent set of approximations per each potential discrete location and evaluation criterion. Then, the fuzzy AHP (Analytic Hierarchy Process) method is used to determine the weights of the different criteria considered from the linguistic evaluations provided by different experts. Finally, we define a set of if-then rules combining the different ANFIS evaluation criteria and their weights within a FIS (Fuzzy Inference System) whose output is used to determine the most convenient location for constructing a solar power farm. The efficacy of the proposed evaluation framework is demonstrated through its application to the Iranian regions of Kerman and Yazd. (C) 2017 Elsevier Ltd. All rights reserved.