Precise solar radiation forecasting for sustainable energy integration: A hybrid CEEMD-SCM-GA-LGBM model for day-ahead power and hydrogen production

In response to the critical need for sustainable energy solutions, this study introduces a groundbreaking hybrid model for enhancing solar radiation forecasting, which is crucial for optimizing solar power integration and hydrogen production. Leveraging a sophisticated amalgamation of Ensemble Empirical Mode Decomposition (EEMD), Sample Entropy-based System Clustering Method (SCM), Genetic Algorithms (GA), and Light Gradient Boosting Machine (LGBM), the model sets a new benchmark in forecasting accuracy. Tested across diverse seasonal data from Jiangsu province, China, it outperforms conventional models like Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks, achieving exceptional accuracy with an average coefficient of determination (R2) of 0.99, root mean square error (RMSE) of 31.17, and mean absolute error (MAE) of 24.03. The model's practical efficacy was validated through a simulated system comprising a photovoltaic array, a DC-DC converter, and an Alkaline electrolyzer, demonstrating a peak hydrogen production of 0.25 kg at 2:30 p.m. on June 18. This indicates the model's superior capability to forecast solar irradiance accurately, which is essential for enhancing the efficiency of solar energy systems and hydrogen generation. The CEEMD-SCM-GA-LGBM hybrid model's adoption is advocated for solar radiation forecasting, promising significant advancements in the integration of solar energy and the optimization of hydrogen production, aligning with global sustainability objectives.