<![CDATA[Accurate wind speed forecasts are critical for integrating wind energy into power grids, reducing imbalance costs in electricity markets, and optimizing wind farm operations. Day-ahead forecasts are typically generated using numerical weather prediction (NWP) models. This work proposes a hybrid model for 24-hour wind speed forecasting, which combines the Weather Research and Forecasting (WRF) model with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The proposed model improves the accuracy of the WRF wind speed forecast through the SARIMA technique by identifying significant autocorrelations in the forecast errors. The study was conducted in La Ventosa, Mexico, a region with significant development in the wind power sector. Wind speed data measured at heights of 17.5 m and 40 m were used during periods of low and high wind speeds. The model’s performance was evaluated using the metrics mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). The results showed that the hybrid WRF-SARIMA model outperformed the WRF model. Forecast errors for MAE were reduced between 29% and 45%, for MSE between 40% and 67%, and for RSME between 22% and 43%. The WRF-SARIMA leverages the benefits of physical NWP models while incorporating the interpretability and reduced computational cost of traditional statistical models. In this way, the proposed model improves wind speed forecast accuracy, especially in the operational contexts of wind energy management.]]>
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