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The rise of green hydrogen as a clean energy source underscores the need for advanced management of renewable energy inputs, particularly photovoltaic (PV) power generation, which is known for its variability and unpredictability. Effective integration and use of PV power is essential for grid stability and sustainable green hydrogen production, which heavily depends on the availability of excess renewable energy. This study introduces a hybrid deep learning model that combines the N-Beats architecture with a sequential model to enhance the accuracy of PV power generation forecasts. The N-Beats model, known for its ability to capture complex temporal relationships, is used alongside a sequential model that processes additional temporal dependencies. Additionally, this study applies Fourier Analysis to identify significant frequency components in the time variable, revealing key elements like yearly, monthly, and daily cycles. The performance of the hybrid model is compared against traditional algorithms like logistic regression, random forests, support vector machines (SVM), and XGBoost, using metrics such as accuracy, Mean Bias Error (MBE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). Our results suggest that the hybrid N-Beats model outperforms traditional algorithms in terms of accuracy and stability, making it a promising option for improving efficiency in green hydrogen production.

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Advancing Photovoltaic Forecasting with Neural Networks: Integrating N-Beats and Sequential Models with Fourier Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Gonzalo Surribas-Sayago,
  • Jose David Fernández-Rodríguez,
  • Enrique Dominguez

摘要

The rise of green hydrogen as a clean energy source underscores the need for advanced management of renewable energy inputs, particularly photovoltaic (PV) power generation, which is known for its variability and unpredictability. Effective integration and use of PV power is essential for grid stability and sustainable green hydrogen production, which heavily depends on the availability of excess renewable energy. This study introduces a hybrid deep learning model that combines the N-Beats architecture with a sequential model to enhance the accuracy of PV power generation forecasts. The N-Beats model, known for its ability to capture complex temporal relationships, is used alongside a sequential model that processes additional temporal dependencies. Additionally, this study applies Fourier Analysis to identify significant frequency components in the time variable, revealing key elements like yearly, monthly, and daily cycles. The performance of the hybrid model is compared against traditional algorithms like logistic regression, random forests, support vector machines (SVM), and XGBoost, using metrics such as accuracy, Mean Bias Error (MBE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). Our results suggest that the hybrid N-Beats model outperforms traditional algorithms in terms of accuracy and stability, making it a promising option for improving efficiency in green hydrogen production.