The combination of fuzzy analytical hierarchical process and maximum entropy methods for the selection of wind farm location.

Abstract

Wind energy is one of the important renewable energy alternatives due to its wide potential and meeting increasing energy demand. However, location selection in wind farms is a complex spatial decision process for decision-makers. This study aimed to determine suitable wind farm locations by combining Fuzzy-Analytical Hierarchical Process (F-AHP) and Maximum Entropy (MaxEnt) methods for Hatay Province, Turkey. Firstly, nine decision criteria for selecting suitable wind farm locations were determined by climate, environmental, social and economic factors. Secondly, the F-AHP and MaxEnt models were implemented and suitable areas were mapped according to five suitability classes. Finally, F-AHP and MaxEnt model results were combined to define and classify priority locations for the wind farm. Study results show that wind speed, air densities and elevation are important criteria for F-AHP, while wind speed, wind power density and distance from power criteria are the most important factors for MaxEnt. Very high and high suitable wind farm locations of Hatay Province cover 21.6% in F-AHP and 29.8% in the MaxEnt model, while very low and low suitable areas cover 48.1% of the study area in both model results. To determine the priority wind farm location, F-AHP and MaxEnt model results were overlapped and reclassified according to the combination of suitability classes. The priority classes show that 62.9% of the study area is unsuitable for the wind farm. However, the limited area was determined as the 1st-priority area (3.2%), 2nd-priority area (4.9%) and 3rd-priority area (6.2%) to locate the wind farm. Consequently, the study methodology enables a more precise evaluation by combining different model results for decision-makers to select the optimum wind farm location selection.