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The wind power industry has experienced remarkable growth due to technological advancements and innovative business models. In 2020, the global installed wind power capacity reached 93 GW, marking a significant 52.96% increase compared to the previous year. This growth highlights the industry’s pivotal role in addressing energy needs and sustainability challenges. Timely wind energy forecasting is critical due to the nonlinear relationship between wind speed and power generation—however, the complexity and uncertainty of natural wind factors present challenges, necessitating effective forecasting methods. A deep learning-based approach named Dense and Dropout Networks (DDN) is introduced to address these challenges, employing Grid Search Optimization techniques. The model consists of eight dense layers for intricate data pattern recognition and a “ReLU” activation function. A dropout layer with a rate of 0.4 is integrated to enhance generalization and mitigate overfitting. The optimization process combines grid search with cross-validation to determine optimal hyperparameters. The actual “Texas Turbine” dataset evaluates the proposed DDN model based on Mean Squared Error (MSE) and Mean Absolute Error (MAE), revealing a significant improvement in accuracy with an enhanced MSE of 94.013% and an improved MAE of 76.947%. In conclusion, the optimized DDN model is a valuable and reliable tool for forecasting wind turbine energy production. Its impressive accuracy and potential for real-world implementation make it a noteworthy contribution to advancing renewable energy technologies and sustainable practices.

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Enhancing Wind Energy Forecasting Efficiency Through Dense and Dropout Networks (DDN): Leveraging Grid Search Optimization

  • 会议论文

  • 发表日期：

    2026年

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  • Talal Alazemi,
  • Mohamed Darwish,
  • Maher Alaraj,
  • Elaf Alsisi

摘要

The wind power industry has experienced remarkable growth due to technological advancements and innovative business models. In 2020, the global installed wind power capacity reached 93 GW, marking a significant 52.96% increase compared to the previous year. This growth highlights the industry’s pivotal role in addressing energy needs and sustainability challenges. Timely wind energy forecasting is critical due to the nonlinear relationship between wind speed and power generation—however, the complexity and uncertainty of natural wind factors present challenges, necessitating effective forecasting methods. A deep learning-based approach named Dense and Dropout Networks (DDN) is introduced to address these challenges, employing Grid Search Optimization techniques. The model consists of eight dense layers for intricate data pattern recognition and a “ReLU” activation function. A dropout layer with a rate of 0.4 is integrated to enhance generalization and mitigate overfitting. The optimization process combines grid search with cross-validation to determine optimal hyperparameters. The actual “Texas Turbine” dataset evaluates the proposed DDN model based on Mean Squared Error (MSE) and Mean Absolute Error (MAE), revealing a significant improvement in accuracy with an enhanced MSE of 94.013% and an improved MAE of 76.947%. In conclusion, the optimized DDN model is a valuable and reliable tool for forecasting wind turbine energy production. Its impressive accuracy and potential for real-world implementation make it a noteworthy contribution to advancing renewable energy technologies and sustainable practices.