Abstract:

Sustainable energy sources like solar and wind speed provide an economically efficient source of energy. Prediction of the output of renewable energy plays a crucial role in shaping decisions concerning electrical system operation and management. Forecasting precision in renewable energy output is essential to ensuring the reliability and stability of the grid, as well as for mitigating risks and minimizing costs within the energy market and power systems. Various statistical techniques were developed to predict solar radiation and wind speed for this purpose and there are two types approaches commonly used: Deep learning and artificial Neural network (ANN). This work propose the used of three statistical methods based in Elman Recurrent Neural Network (ERNN), Long Short Term Memory (LSTM) and Support Vector Machine (SVM) to forecast the output data in different forecasting horizons. Four evaluation deferent metric are used: Forecast skill (FS), Root mean square error (RMSE), Mean Absolute Error (MAE) and coefficient of determination ( R²). These metrics confirm the robustness and accuracy of the LSTM model, validated by its RMSE, MAE, FS, and R² values for both sites. These performances demonstrate the effectiveness of LSTM in capturing temporal patterns, with significant implications for weather forecasting and renewable energy applications.

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