A fuzzy time series forecasting model with both accuracy and interpretability is used to forecast wind power

Considering the current research focus on the interpretability and efficiency of wind speed prediction models, this research presents a novel prediction model for dynamic non-stationary fuzzy time series. The proposed model aims to effectively improve prediction accuracy and address the issues of low interpretability and excessive data preprocessing. Unlike existing mainstream hybrid systems for wind speed prediction, this model provides detailed explanations for almost every prediction step and eliminates the need for cumbersome data preprocessing steps. To improve the prediction accuracy of the proposed model, this research incorporates non-stationary sets to overcome the limitations of fuzzy time series in adapting to long-term changes. The developed algorithm, SFTSM, dynamically adjusts the fuzzy time series prediction to effectively address long-term prediction challenges. Furthermore, this study introduces an enhanced version of the artificial hummingbird algorithm, called SLG-AHA, to further improve the accuracy and stability of fuzzy time series prediction. Experimental results using data from the Shandong Penglai wind farm in China validate the effectiveness of the proposed model by showcasing its superior prediction accuracy and stability.