A novel fuzzification-forecasting-optimization ensemble system for wind speed based on fuzzy theory and a multiobjective optimizer

The development of a new energy generation industry, represented by wind power generation, constitutes a crucial endeavor in attaining the dual carbon goal. Nonetheless, the high-dimensional, nonlinear, and stochastic nature of wind speed sequences imposes substantial demands on power systems' peak regulation capability. To address the limitations of traditional prediction methods, which struggle to provide probabilistic prediction outcomes and exhibit poor generalizability, this paper introduces a fusion of fuzzy theory and advanced artificial intelligence algorithms. The proposed approach, termed the fuzzification-forecasting-optimization ensemble system (FFOES), enables both point and interval predictions of wind speed, thereby complementing the emerging literature on power system dispatch. By leveraging fuzzy set theory and the concept of information granulation, the original sequence is initially divided into manageable particles of smaller scales. Subsequently, the computational efficiency of shallow neural networks and the automatic feature extraction capability of deep learning are harnessed to construct a comprehensive and integrated strategy for wind speed prediction. The numerical results demonstrate that the FFOES achieves a superior balance between prediction accuracy and stability compared to that of 15 benchmark models. Notably, the system's robust performance across distinct sites and time intervals underscores the significant potential of the FFOES in the realm of wind system risk management.