Retraction Notice: New Deep Learning-Based Approach for Wind Turbine Output Power Modeling and Forecasting

An intelligent machine learning-based method is developed in this article for modeling and prediction of the wind turbine (WT) output power. The developed technique makes use of the advanced machine-learning models for creating prediction intervals around the WT power samples. Compared with other techniques, which make a point-by-point prediction, the proposed technique can capture the uncertainty of the forecast error generating lower and upper bounds around the samples. Moreover, a fuzzy min–max technique is employed to get into a Nash equilibrium between the prediction confidence level and the average prediction bandwidth. In order to get reliable results, two support vector machine is deployed, which compete with each other to improve the prediction performance. This game can potentially contribute to a highly accurate prediction model. Also, a new optimization method based on the flower pollination algorithm is developed due to the high complexity and nonlinearity of the modeling, which can search for the most optimal structure of the model. The practicability and suitable performance of the method are judged by means of the experimental data in Australian islands.