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Enhancing green hydrogen forecasting with a spatio-temporal graph convolutional network optimized by the Ninja algorithm
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DOI：10.1038/s41598-025-23084-x.png)
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Abstract
En 中文
In light of increased international efforts to combat climate change, sustainable infrastructure is shifting toward green hydrogen produced through renewable-powered electrolysis. Still, it is challenging to forecast the production of green hydrogen because environmental and system factors are variable both in time and space. We introduce a new system that utilizes a Spatio-Temporal Graph Convolutional Network (STGCN) and a novel algorithm, the Ninja Optimization Algorithm (NiOA), to address this issue. Using the framework, binary NiOA performs feature selection, while continuous NiOA optimizes both the model architecture and the number of variables in the data simultaneously. It is clear from the research that forecasting results have shown significant improvement. The STGCN model achieved an R2 of 0.8769 and an MSE of 0.00375, whereas the STGCN with NiOA reached an R2 of 0.9815 and an MSE of only $$7.48 \times 10^{-8}$$ . Due to these improvements, adaptive metaheuristics show even greater promise in delivering more accurate forecasting and reduced computational requirements for addressing critical environmental issues. The suggested strategy can be followed repeatedly, providing a solid framework for the effective modeling of renewable energy systems and making green hydrogen projects more dependable.
Keywords:
Green hydrogen forecasting
Spatio-temporal graph convolutional network (STGCN)
Metaheuristic optimization
Ninja optimization algorithm (NiOA)
Feature selection
Hyperparameter tuning
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