Environmental considerations have prompted the use of renewable energy resources worldwide for reduction of greenhouse gas emissions. An accurate prediction of wind speed plays a major role in environmental planning

energy system balancing

wind farm operation and control

power system planning

scheduling

storage capacity optimization

and enhancing system reliability. This paper proposes an accurate prediction of wind speed based ona Recursive Radial Basis Function Neural Network (RRBFNN) possessing the three inputs of wind direction

temperature and wind speed to improve modern power system protection

control and management. Simulation results confirm that the proposed model improves the wind speed prediction accuracy with least error when compared with other existing prediction models.