In response to environmental pollution and the energy crisis, countries worldwide are actively advancing green wind power. However, owing to uncontrollable variables such as wind speed and direction, wind power generation is characterized by volatility and intermittency, necessitating accurate forecasting of wind power output to ensure the secure operation of integrated energy systems. To mitigate this challenge, this paper proposes a wind power forecasting method based on a dual-tower Transformer model, which integrates multiple features. This approach thoroughly investigates wind power data enriched with meteorological information, extracting feature information comprehensively. The method incorporates temporal attention, empirical distribution, and K-means clustering to construct a Transformer model that decouples meteorological patterns by considering temporal dependencies and distributional traits. Additionally, feature-level attention, Hilbert transform, and seasonal trend decomposition are integrated to build a Transformer model for seasonal trend decomposition that leverages feature associations and frequency-domain characteristics. Finally, a weighted average fusion module is proposed to combine the predictions of the two aforementioned Transformer models, thereby enhancing the accuracy of wind power forecasting. Extensive experimental comparisons with advanced algorithms from the past five years, conducted on both general public datasets and proprietary datasets from enterprises, reveal that the proposed method markedly outperforms existing techniques in terms of both prediction accuracy and model interpretability. In comparison with the runner-up algorithms, the proposed method reduces the mean squared error (MSE) and the mean absolute error (MAE) by up to 15.34% and 6.04%, respectively, while enhancing the coefficient of determination R2 by as much as 39.33%.