earticle

영어

This paper designs and develops a wind speed forecast model based on artificial neural networks and voting strategies, aiming at efficiently integrating renewable energies into the power network. The hour-by-hour speed records collected in Jeju city during the past 15 years are classified and converted to create sequential, monthly, and seasonal forecast models, respectively. To predict the next hour speed, the speed records of the previous 5 hours are simultaneously fed to each model first. Then, the voting process picks and averages the two predictions having the best proximity out of 3. The evaluation procedure compares the predicted values and the actual speeds of 2014, which have not been used for training, and finds out that the maximum daily root mean square error for the proposed scheme is smaller than other stand-alone methods by 0.11mps. Moreover, the vote-based scheme avoids the worst case mis-prediction.