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March 29, 2026IET Renewable Power Generation0 citationsOpen Access

Ultra-Short-Term Power Forecasting Using CNN-BiGRU for PV Systems

Ultra‐Short‐Term Power Forecasting of Single‐Axis Tracking PV System Based on Irradiance Transformation and CNN‐BiGRU Model

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Authors

YXYi XiaYSYing SuXYXuan Yu

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Overview

Demonstrates improved power forecasting in PV systems using a novel CNN-BiGRU model and irradiance transformation.

Key Points

  • The aim is to enhance the accuracy of ultra-short-term power forecasting for single-axis tracking PV systems.
  • Developed a forecasting method based on irradiance transformation and CNN-BiGRU model.
  • Used FCM clustering to categorize weather types for better model adaptability.
  • Validated using two-year operational data from PV plants in China.
  • Achieved low MSE values for different weather conditions in both PV plants.
  • Improved MAE, RMSE, R2, and FS metrics compared to existing models.
  • Demonstrated significant performance enhancement due to the approach.

Cite This Study

Xia et al. (2026) studied this question.

synapsesocial.com/papers/69c8c35cde0f0f753b39e227https://doi.org/10.1049/rpg2.70214
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