Open Access

ARTICLE

Spatio-Temporal Graph Neural Networks with Elastic-Band Transform for Solar Radiation Prediction

Guebin Choi^*

Department of Statistics (Institute of Applied Statistics), Jeonbuk National University, Jeonju, 54896, Republic of Korea

* Corresponding Author: Guebin Choi. Email:

(This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)

Computer Modeling in Engineering & Sciences 2026, 146(1), 27 https://doi.org/10.32604/cmes.2025.073985

Received 29 September 2025; Accepted 05 December 2025; Issue published 29 January 2026

Abstract

This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series. Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks (STGNNs). However, such definitions are prone to generating spurious correlations due to the dominance of periodic structures. To address this limitation, we adopt the Elastic-Band Transform (EBT) to decompose solar radiation into periodic and amplitude-modulated components, which are then modeled independently with separate graph neural networks. The periodic component, characterized by strong nationwide correlations, is learned with a relatively simple architecture, whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions. The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability. The proposed framework is validated with multiple STGNN architectures, and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods.

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Keywords

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