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Solar radiation prediction using multivariate signal decomposition and physics-informed time-frequency feature

Xingchen Mo1, Jingzhou Xin2, Yan Jiang3,4

  • 1State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing, China.

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Accurate solar radiation prediction is vital for solar energy systems. This study introduces a novel method using advanced data preprocessing and a frequency-domain physics-informed convolutional network (FD-PICN) for improved forecasting.

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Area of Science:

  • Renewable Energy Systems
  • Data Science and Analytics
  • Physics-Informed Machine Learning

Background:

  • Accurate solar radiation forecasting is essential for optimizing solar energy system design and photovoltaic power grid integration.
  • The complex dynamic characteristics of solar radiation data present significant challenges for precise prediction.
  • Existing methods often struggle to capture the intricate spatial-temporal-frequency dynamics inherent in solar radiation data.

Purpose of the Study:

  • To develop a robust solar radiation prediction method capable of handling complex spatial-temporal-frequency characteristics.
  • To enhance the efficiency of solar energy system design and photovoltaic power grid integration through accurate forecasting.
  • To address the limitations of current prediction techniques by incorporating physical insights into a deep learning framework.

Main Methods:

  • High-quality data preprocessing using multivariate fast iterative filtering for synchronous decomposition of multi-station solar radiation data.
  • Development of a frequency-domain physics-informed convolutional network (FD-PICN) to capture solar radiation evolution patterns.
  • Integration of cross-attention-assisted time-frequency feature extraction and physical coherence functions (frequency-domain coherence function, phase lock value) within the FD-PICN model.

Main Results:

  • The proposed method effectively decomposes multi-station solar radiation data, considering spatiotemporal and time-frequency correlations.
  • FD-PICN successfully captures complex evolution patterns by integrating time-frequency features and physical coherence.
  • Numerical examples using measured data demonstrate superior performance compared to existing methods across various predictive scenarios.

Conclusions:

  • The presented solar radiation prediction method, combining advanced preprocessing and FD-PICN, offers high-performance forecasting capabilities.
  • This approach effectively handles the complex spatial-temporal-frequency characteristics of solar radiation data.
  • The findings validate the method's potential for optimizing solar energy systems and improving grid integration.