<![CDATA[Wind speed forecasting plays a vital role in various sectors, including renewable energy management and disaster preparedness for extreme weather events. Accurate prediction models are essential to support decision-making processes, especially in regions with dynamic seasonal patterns. This study compares the performance of three time series prediction models Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Radial Basis Function Neural Network (RBFNN) for forecasting daily wind speed. The dataset consists of historical wind speed data that underwent multiple preprocessing steps, including seasonal-based missing value imputation, stationarity testing, supervised transformation, normalization, and hyperparameter tuning to optimize model performance. The models were evaluated using four standard regression metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-Squared (R²), and Mean Absolute Percentage Error (MAPE). The results show that the TCN model outperformed the others, achieving an MAE of 1.117, RMSE of 1.524, R² of 0.120, and MAPE of 20.95%. The LSTM model ranked second with competitive performance, while the RBFNN model produced consistent but slightly lower accuracy. The findings highlight the superiority of TCN in capturing complex sequential and seasonal patterns in wind speed data. The unique contribution of this research lies in integrating seasonal-based preprocessing with a comparative evaluation of three advanced models under varying conditions, including extreme weather scenarios. This study serves as a foundation for developing more accurate and reliable wind speed forecasting systems to support renewable energy planning and enhance disaster risk mitigation strategies.]]>
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