Autor(es)

Albuquerque, Mateus Vasconcelos ; Casaca, Wallace

• Tipo de documento: Artigo Científico
• Fonte: COMPUTATIONAL SCIENCE AND ITS APPLICATIONS-ICCSA 2025, PT III; v. 15650, p. 18-pg., 2025-01-01.

Resumo

The transition to renewable energy sources has generated increased interest in accurate forecasting methods for green hydrogen production. This work aims to predict green hydrogen production from solar energy generation using machine and deep learning models. The proposed approach includes data preprocessing from different sites, implementing AI-driven techniques, running hyperparameter optimization, and extrapolating these parameters for training with real data from other sites. In addition, the Time Delay Embedding technique is applied to capture the temporal dependencies of the data for supervised learning. The methods Random Forest, Support Vector Regression, Extreme Gradient Boosting, and Long Short-Term Memory are trained and properly tuned. The results demonstrate that Extreme Gradient Boosting model achieves the highest accuracy, with all models adapting well to data from the distinct stations analyzed. The extrapolation of optimized hyperparameters proved efficient, reducing computational costs without compromising accuracy. In conclusion, the proposed approach is robust and viable for predicting the production of green hydrogen at different locations, making it a scalable solution for supporting clean energy planning. (AU)

• Processo FAPESP: 23/14427-8 - Ciência de Dados para a Indústria Inteligente (CDII)
• Beneficiário: José Alberto Cuminato
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs