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Advanced solar radiation prediction using combined satellite imagery and tabular data processing.

Mohammed Attya¹, O M Abo-Seida², H M Abdulkader³

¹Department of Information System, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt. mohammed3attya@gmail.com.

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|April 24, 2025

Summary

This study enhances solar radiation prediction by combining satellite images and tabular data using a novel hybrid approach. The method improves forecast accuracy, vital for optimizing solar energy systems.

Keywords:

GANs Identity block LSTM Latent diffusion model Solar radiation

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Area of Science:

Renewable Energy Systems
Artificial Intelligence in Environmental Science

Background:

Accurate solar radiation prediction is essential for efficient solar energy system operation.
Existing methods often rely on single data sources, limiting prediction accuracy.
Integrating diverse data types, like satellite imagery and ground-based measurements, offers potential for improvement.

Purpose of the Study:

To develop and validate a hybrid methodology for enhanced solar radiation prediction.
To improve the accuracy of solar radiation forecasts by integrating satellite imagery and tabular satellite data.
To demonstrate the effectiveness of the proposed approach for renewable energy applications.

Main Methods:

A two-path hybrid framework was developed: one for satellite image processing (noise removal, pixel imputation, feature identification) and another for tabular data imputation.
Satellite image processing involved latent diffusion models for noise reduction and a modified Generative Adversarial Network (GAN) for pixel imputation.
Tabular data imputation utilized diffusion models, followed by feature selection and Long Short-Term Memory (LSTM) networks for final solar radiation prediction.

Main Results:

The proposed hybrid methodology significantly improved solar radiation prediction accuracy compared to existing techniques.
Key components, including noise removal, pixel imputation, and data imputation, demonstrated high efficiency.
The integration of both data paths before the prediction stage led to enhanced overall model performance.

Conclusions:

The hybrid approach effectively integrates satellite imagery and tabular data for superior solar radiation forecasting.
The developed imputation and noise reduction techniques are efficient and contribute to prediction accuracy.
This research provides a robust framework for optimizing solar energy systems through improved prediction capabilities.