Wind energy is a core pillar of global green and sustainable energy transition. However, existing wind power prediction models face three key challenges: traditional long short-term memory (LSTM) models struggle to capture long-term temporal dependencies efficiently and have high training latency, while Transformer-based models exhibit excessive computational complexity and are prone to overfitting for short-term fluctuating data; meanwhile, few models integrate seasonal trend modeling with multi-scale temporal feature extraction, leading to large prediction errors in seasonal transitions. To address these issues, this paper proposes a hybrid prediction framework combining a novel T-LSTM recurrent unit with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The T-LSTM unit fuses a simplified Transformer module and an improved LSTM structure. Thus, the design can synergistically capture both short-term fluctuations and long-term dependencies in wind power data. Complementarily, SARIMA is integrated via weighted fusion to model seasonal trends, addressing the neglect of seasonal characteristics in existing deep learning models. A diverse set of benchmark methods for wind power prediction are selected for comparison, including LSTM, convolutional neural network-gated recurrent unit (CNN-GRU), nsTransformer, Autoformer, Reformer and least squares support vector machine (LSSVM), with experiments conducted across various prediction horizons. The results show that the proposed T-LSTM model outperformed most benchmark methods in key evaluation metrics across multiple prediction horizons and exhibited no statistically significant difference from Autoformer only in the 90 min horizon, validating its superiority in handling complex wind power time series.
Zhong et al. (Thu,) studied this question.