A Multi-Strategy-Optimized Echo State Network Model: Global Horizontal Irradiance Forecasting for Photovoltaic Water Electrolysis Hydrogen Production Systems

The intermittent nature of solar irradiance introduces substantial variability into photovoltaic water electrolysis hydrogen production systems (PWEHPSs), posing significant challenges to stable power scheduling and hydrogen production. Accurate forecasting of global horizontal irradiance (GHI) is therefore essential for improving operational reliability; however, existing studies still offer insufficient predictive accuracy and inadequate system-level validation for reliable electrolyzer-coupled operation. To address this gap, this work develops an ESN-based GHI forecasting model and employs a multistrategy improved projection iteration optimization algorithm (MSPIMO) to tune key hyperparameters, including reservoir size, leakage rate, spectral radius, and input scaling coefficient. An integrated evaluation framework is further established to link irradiance forecasting with photovoltaic power output prediction and hydrogen production assessment in a proton-exchange-membrane-electrolyzer-based PWEHPS. Using multisource meteorological data from the National Solar Radiation Database for Lanzhou, Gansu Province, China, the proposed model was trained, validated, and evaluated over 30 independent runs. The model achieved mean MAE, RMSE, MAPE, and R 2 values of 15.075 W/m 2, 20.620 W/m 2, 13.82%, and 0.993, respectively. Compared with the WOA-EESN, the best-performing benchmark model, the proposed model reduced MAE and RMSE by 40.6% and 39.6%, respectively. In the downstream PWEHPS application, the relative error for monthly cumulative hydrogen production was 0.50%, while the daily and weekly relative errors remained within 0.66% and 0.28% to 0.72%, respectively. These results demonstrate that the proposed approach improves the GHI forecasting accuracy and enables more reliable downstream hydrogen production estimation under the studied scenario.