With the large-scale integration of wind power into the grid, the accuracy of wind farm cluster power prediction has become a key factor for the sustainability of modern power systems. Reliable ultra-short-term forecasts support the secure dispatch of high-penetration renewable energy, reduce wind curtailment, and improve the low-carbon and economical operation of power systems. Aiming at the problem of significant differences in wind turbine characteristics, this paper proposes a prediction method based on an improved density-based spatial clustering of applications with noise (DBSCAN) and a hybrid deep learning model. First, the wind speed signal is decomposed at multiple scales using successive variational modal decomposition (SVMD) to reduce non-stationarity. Subsequently, the DBSCAN parameters are optimized by the fruit fly optimization algorithm (FOA), and dimensionality reduction is performed by principal component analysis (PCA) to achieve efficient clustering of wind turbines. Next, the representative turbines with the highest correlation are selected in each cluster to reduce computational complexity. Finally, the SVMD-TCN-BiGRU-MSA-GJO hybrid model is constructed, and long-term dependence is extracted using a temporal convolutional network (TCN); the temporal features are captured by bidirectional gated recurrent units (BiGRUs); the feature weights are optimized by a multi-head self-attention mechanism (MSA), and the hyper-parameters are, in turn, optimized by golden jackal optimization (GJO). The experimental results show that this method reduces the MAE, RMSE, and MAPE by 14.02%, 12.9%, and 13.84%, respectively, and improves R2 by 3.9% on average compared with the traditional model, which significantly improves prediction accuracy and stability. These improvements enable more accurate scheduling of wind power, lower reserve requirements, and enhanced stability and sustainability of power system operation under high renewable penetration.
Gao et al. (Sun,) studied this question.