Weighted parallel algorithm to improve the performance of short-term wind power forecasting

The increased integration of wind power into the electric grid poses new challenges due to its fluctuation and volatility. Accurate short term wind power forecasting is one of the most effective ways to mitigate these challenges. As every forecasting algorithm has its advantages and weaknesses, the forecasting accuracy varies when these models are applied to different wind farms due to non-uniform characteristics of wind patterns. Therefore, a weighted parallel algorithm which combines the individual forecasting models together is proposed. For variable data from a wind farm, the model can adjust and optimize portion of individual models. Compared with each single model, the weighted parallel algorithm has better robust adaptation which can improve the forecasting precision.