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Wind  energy as one of the new renewable energies has an important role in replacing fossil energy sources in Indonesia. In order to make the wind turbine's performance more efficient in extracting energy from the wind, it is necessary to control the actuation movements pitch and yaw of the wind turbine horizontal . Controlling the actuator yaw can increase the absorption efficiency of the power to the rotor face toward the direction of the wind. The purpose of this thesis is to be able to predict the direction of the coming wind, then move the turbine rotor in the predicted direction. In this final project a wind turbine prototype is used with a precision of 5.3%, then for the data acquisition section, a wind direction sensor is built to change the amount of wind direction to a quantity that can be measured in units of degrees, and anemometer to measure wind speed. In making the wind direction prediction algorithm, artificial neural network (ANN) method is used with input parameters such as wind speed, temperature, humidity, pressure, and altitude. Data acquisition is done at one minute intervals with long data collection for one day, 1072 data are obtained, the data is then fed to the ANN model that has been prepared. Based on the results of tests that have been done, it is found that the Mean Absolute Error in the model is 0.4%.

Design and Implementation of Artificial Neural Networks to Predict Wind Directions on Controlling Yaw of Wind Turbine Prototype