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Related Experiment Videos

A Hybrid CNN-GRU-SE Forecasting Method for Short-Term Photovoltaic Power Considers AFD and Data Aggregation.

Keyan Liu1, Dongli Jia1, Huiyu Zhan1

  • 1China Electric Power Research Institute, Beijing 100192, China.

Entropy (Basel, Switzerland)
|May 26, 2026
PubMed
Summary

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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:

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This study introduces a new method for accurate short-term photovoltaic (PV) power forecasting. The novel approach significantly improves prediction accuracy and robustness, outperforming existing models.

Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Energy
  • Power Systems Engineering

Background:

  • Accurate short-term photovoltaic (PV) power forecasting is crucial for grid stability and energy management.
  • Existing forecasting methods often struggle with volatility and complex data patterns.
  • Integrating diverse data sources and advanced algorithms can improve PV power prediction.

Purpose of the Study:

  • To develop a novel, highly accurate, and robust short-term PV power forecasting method.
  • To enhance forecasting by integrating spatial correlations and adaptive signal decomposition.
  • To optimize model parameters and analyze feature contributions for improved interpretability.

Main Methods:

  • Feature selection using Pearson correlation and entropy weight method.
Keywords:
CNN-GRUadaptive frequency decompositionbeluga whale optimizationdata aggregation

Related Experiment Videos

  • Spatial data aggregation for neighboring PV stations.
  • Adaptive Frequency Decomposition (AFD) for time series decomposition.
  • Modified Improved Beluga Whale Optimization (MIBWO) for parameter tuning.
  • CNN-GRU-SE hybrid model for forecasting.
  • SHAP method for model interpretability.

    • Main Results:

    • The proposed method significantly enhances PV power forecasting accuracy and robustness.
    • Achieved substantial reductions in Mean Absolute Error (MAE) compared to baseline models (96.23% and 95.03%).
    • Demonstrated stable performance under both sunny and cloudy weather conditions.
    • SHAP analysis provided insights into feature importance for prediction.

    Conclusions:

    • The integrated approach of data aggregation, AFD, MIBWO, and CNN-GRU-SE offers superior PV power forecasting.
    • The method effectively handles output volatility and exploits spatial-temporal dependencies.
    • The proposed technique provides a reliable and interpretable solution for short-term PV power prediction.