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A Cloud Detection Network Based on Adaptive Laplacian Coordination Enhanced Cross-Feature U-Net.

Kaizheng Wang1, Ruohan Zhou1, Jian Wang1

  • 1Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, P.R. China.

International Journal of Neural Systems
|December 14, 2024
PubMed
Summary

Accurate cloud detection is vital for solar power forecasting. A new method, ALCU-Net, enhances cloud identification, improving photovoltaic power generation predictions.

Keywords:
Cloud detectionadaptive feature coordinationcross-featurelaplacian operator

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Area of Science:

  • Atmospheric Science
  • Renewable Energy Technology
  • Computer Vision

Background:

  • Cloud cover variability significantly impacts solar irradiance and photovoltaic (PV) power output.
  • Precise detection of thin, fragmented clouds is essential for reliable PV power forecasting.

Purpose of the Study:

  • To introduce a novel cloud detection method, ALCU-Net, for improved accuracy in PV power generation forecasting.
  • To enhance the U-Net architecture with specialized modules for better cloud feature extraction and spatial coherence.

Main Methods:

  • Developed Adaptive Laplacian Coordination Enhanced Cross-Feature U-Net (ALCU-Net).
  • Incorporated Adaptive Feature Coordination (AFC), Multi-Grained Laplacian-Enhanced (MLE) features, and Criss-Cross Feature Fused Detection (CCFE) modules.
  • Augmented traditional U-Net with enhanced spatial coherence, hierarchical feature integration, and refined edge detection.

Main Results:

  • ALCU-Net demonstrated superior performance compared to existing cloud detection methods.
  • Achieved high accuracy in identifying both thick and thin clouds.
  • Successfully mapped fragmented cloud patches across diverse environments (ocean, polar, ocean-land mixtures).

Conclusions:

  • ALCU-Net offers a significant advancement in cloud detection for solar energy applications.
  • The method's robustness across various environments makes it suitable for real-world PV forecasting.
  • Improved cloud detection accuracy directly translates to more reliable photovoltaic power generation predictions.