Hybrid neurofuzzy investigation of short-term variability of wind resource in site suitability analysis: a case study in South Africa

Energy generation from wind resources is now a mature technology with the ability to compete with traditional energy sources at utility scales in many countries, through the identification of suitable sites. However, beyond site suitability, predicting the wind resource variability of the potentially viable site presents overarching benefits in strategic and operational planning prior to site development. This study, therefore, combines geographical information systems multicriteria decision-making (GIS-MCDM) and hybrid neurofuzzy modeling tools for site suitability and resource variability forecast, respectively, in the Eastern Cape Province of South Africa. The GIS model uses two factors (climatological and environmental), and analytical hierarchical process was used for evaluating criteria degree of influence. Wind resource variability using diurnal satellite-based data for the candidate site was used on the models. Adaptive neurofuzzy inference system models hybrid with genetic algorithm (GA-ANFIS) and particle swarm optimization (PSO-ANFIS) were compared with standalone ANFIS and Levenberg–Marquardt backpropagation neural network (LMBP-ANN) using six statistical measures of error, accuracy, and variability. The GA-ANFIS and PSO-ANFIS accurately model the resource with PSO-ANFIS having lesser computational time compared to GA-ANFIS. However, LMBP-ANN is most robust and resistant in modeling the resource variability among the four models. Hence, wind resource variability investigation on a potentially viable site obtained from the GIS-MCDM model can complement on-site investigations prior to site development. Also, tuning ANFIS with evolutionary algorithms offers improved accuracy over standalone ANFIS model for wind resource forecast and further its robustness in predicting variability of the resource. From our findings, cross-boundary wind resource exploration between South Africa and Lesotho could foster regional interconnectivity.