The main objective of this study is to develop a reliable model for the identification of locations for the installation of wind farms, which will provide significant support to planners in the strategy for the development and management of wind energy. The proposed model is based on the combined application of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) using the multi-criteria technique of Decision Making Trial and Evaluation Laboratory (DEMATEL), the Analytic Network Process (ANP) and Multi-Attributive Border Approximation area Comparison (MABAC). Application of the model is presented by means of a case study on the province of Vojvodina, Serbia. The model considers 11 constraints and 11 evaluation criteria which are grouped into economic, social and environmental clusters. The DEMATEL-ANP (DANP) method is used to determine the weight coefficients of the evaluation criteria, and the MABAC method is used to rank the selected viable locations. The final map of benefits is presented using raster cells (alternatives) which are evaluated in the range of 1 (least suitable) to 7 (most suitable). The results show that an area of 321 km(2) in Vojvodina is very suitable for the installation of wind farms. Ranking of viable locations using the MABAC method shows that a location in the vicinity of the village of Laudonovac (L8) is most suitable for the installation of wind farms in the province of Vojvodina. A sensitivity analysis, carried out by changing the input weights of the clusters, indicates that the model is useful for identifying suitable locations for the development of wind farm projects, as well as for assessing the suitability of already licensed projects for the construction of wind farms. The proposed method and the results of this study can be used for spatial development policy at all levels of public administration related to renewable energy resources. The model could also help to successfully identify suitable locations for the installation of wind farms in other areas with similar geographical conditions. (C) 2016 Elsevier Ltd. All rights reserved.