Work

GAN-based solar radiation forecast optimization for satellite communication networks

Year: 2025

Type: article

Abstract: Accurate short-term solar radiation forecasting is essential for the stable operation and dispatch of photovoltaic power generation systems. Advanced encoder–decoder architectures, utilizing satellite remote sensing data, are now primary techniques for this forecasting task. However, these methods encounter significant limitations, particularly as forecast horizons extend. In such scenarios, predictions often exhibit spatial texture degradation and distortions in radiation intensity. This significantly reduces precision and reliability, making it difficult to meet the demands of high-precision applications. To address these limitations, this paper proposes GAN-Solar, a novel quality optimization model for short-term solar radiation forecasting based on Generative Adversarial Networks (GANs). GAN-Solar utilizes an ED-AttUNet model, enhanced with conditional inputs, as its generator. A discriminator, incorporating residual structures, progressive downsampling, and conditional information, is employed to distinguish between real and generated forecasts. This adversarial process, guided by a hybrid loss function and a discriminator treated as a learnable objective function, refines the forecast quality. Experimental results on summer solar radiation data demonstrate that GAN-Solar significantly improves forecast quality. It reduces the Root Mean Square Error by approximately 3.2% and increases the Structural Similarity Index from 0.84 to 0.87 when compared to the baseline ED-AttUNet model. The proposed method effectively mitigates issues of texture degradation and intensity distortion, leading to clearer and more accurate solar radiation predictions. • Proposes GAN-Solar to enhance short-term solar radiation forecast accuracy and realism. • Designs a GAN with ED-AttUNet generator and a discriminator using conditional inputs. • Validates GAN-Solar on real data, showing improved metrics and visual forecast quality.

Source: International Journal of Intelligent Networks

DOI: 10.1016/j.ijin.2025.07.004

Authors: Chao Chen, Xin Liu, Shizhong Zhao, Muhammad Bilal

Institutions: Nanjing University of Information Science and Technology, China Meteorological Administration, Lancaster University

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $700