Stacked Gated Recurrent Unit Approach for Wind Speed Forecasting in the Region of Adrar
Wind power has seen increased growth and development in recent years as a clean, low-cost renewable energy type. Wind speed forecasting has been playing an important role in the generation of wind power as a key to improving the accuracy of wind power and the integration of renewable energy within the main electrical grid and its stability. This paper proposes a stacked gated recurrent unit SGRU model for short-term wind speed forecasting in the Adrar wind station, using the NASA dataset. The suggested method efficacy is evaluated using data from the Adrar wind farm. In comparison to existing deep learning-based approaches, the suggested strategy is appropriate for wind speed forecasting and achieves superior forecasting performance in terms of several index errors.
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