Adaptive neuro-fuzzy estimation of building augmentation of wind turbine power

Wind power is generating interest in many countries as a way to produce inexpensive and sustainable electrical power. Building integrated wind turbines (BIWTs) are an interesting option in this respect. BIWTs are low cost renewable sources of energy. Since the power in wind is proportional to the cubic power of the wind velocity approaching the wind turbine, a small amount of wind speed acceleration leads to a large increase in energy output. To augment free wind speed streams, the open area between two buildings can be used as diffuser by taking advantage of the Venturi effect. A system where two buildings are used to increase the winds kinetic energy is called building augmented wind turbine (BAWT). This article shows that the shape of buildings can be changed to maximize the power generated by wind and power augmentation. To estimate building power augmentation using a simplified turbine model, this paper constructed a process that simulated the augmented power and wind velocity in regard to different building geometries using an adaptive neuro-fuzzy (ANFIS) method. This intelligent estimator was implemented using MATLAB/Simulink. The simulation results presented in this paper demonstrated the effectiveness of the method developed in this study.