Abstract
It is important to forecast the wind speed range for managing the operation of wind turbines (WTs). Since the electrical power generated by WTs is highly dependent on the uncertain inherent of atmosphere meteorology, improving the accuracy of wind speed range forecasting models leads to the improvement of wind generation prediction. For the sake of uncertainties, it is very challenging to develop an effective and practical model to achieve accurate wind speed range forecasting in large forecasting horizons. This paper presents a novel hybrid classifier based on extended-classifier system with real input (XCSR) and an adaptive neuro-fuzzy inference system (ANFIS), for classification of the wind speed range. It should be mentioned that by employing a cooperative fuzzy classifier system (XCSR–ANFIS), the accuracy and number of the rules that XCSR system must be learned during the training process in the proposed model will be higher and fewer than the XCSR model, respectively. Finally, the comparison of obtained results by implementing the proposed model with other methods for long and short horizons confirms the desirable performance of the proposed model.
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Alipour, M., Aghaei, J., Norouzi, M. et al. A Novel Cooperative Fuzzy Classifier for Predicting the Permissible Wind Speed Range in Wind Farms. Iran J Sci Technol Trans Electr Eng 45, 29–45 (2021). https://doi.org/10.1007/s40998-020-00347-z
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DOI: https://doi.org/10.1007/s40998-020-00347-z