A Wind Power Prediction Approach on the Grounds of FCM Fuzzy Clustering and TCN–Transformer

With the goal of achieving more accurate wind power predictions by accounting for meteorological influences comprising wind speed, together with wind direction and air pressure, this thesis proposes a method combining fuzzy C-means (FCM) clustering with a TCN–Transformer hybrid model. After preprocessing the data to remove outage and missing records, we apply the Pearson correlation coefficient to identify average wind speed and wind direction that are suitable to serve as input features for the model, together with the atmospheric pressure, as key input features. FCM clustering is then applied to partition the data into low- and high-wind-speed operating conditions, mitigating the accuracy loss caused by uniform modeling. A TCN–Transformer model is subsequently constructed, integrating local temporal feature extraction with global dependency modeling to perform prediction under each condition. The experimental results demonstrate that the proposed FCM–TCN–Transformer framework consistently achieves superior forecasting performance under both low-wind-speed and high-wind-speed conditions. Compared with benchmark models, including TCN, LSTM, GRU, BiGRU, and Transformer, the proposed method achieves lower prediction errors and higher prediction accuracy across different forecasting horizons. Furthermore, repeated experiments with multiple random seeds verify the robustness and stability of the proposed framework. These results indicate that FCM-based wind regime classification effectively reduces data heterogeneity, while the hybrid TCN–Transformer architecture successfully captures both local temporal patterns and long-range temporal dependencies. Therefore, the proposed framework provides an effective and reliable solution for short-term wind power forecasting and contributes to the secure integration of wind energy into modern power systems.