The yield of photovoltaic hydrogen production systems is influenced by a number of factors, including weather conditions, the cleanliness of photovoltaic modules, and operational efficiency. Temporal variations in weather conditions have been shown to significantly impact the output of photovoltaic systems, thereby influencing hydrogen production. To address the inaccuracies in hydrogen production capacity predictions due to weather-related temporal variations in different regions, this study develops a method for predicting photovoltaic hydrogen production capacity using the long short-term memory (LSTM) neural network model. The proposed method integrates meteorological parameters, including temperature, wind speed, precipitation, and humidity into a neural network model to estimate the daily solar radiation intensity. This approach is then integrated with a photovoltaic hydrogen production prediction model to estimate the region’s hydrogen production capacity. To validate the accuracy and feasibility of this method, meteorological data from Lanzhou, China, from 2013 to 2022 were used to train the model and test its performance. The results show that the predicted hydrogen production agrees well with the actual values, with a low mean absolute percentage error (MAPE) and a high coefficient of determination (R²). The predicted hydrogen production in winter has a MAPE of 0.55% and an R² of 0.985, while the predicted hydrogen production in summer has a slightly higher MAPE of 0.61% and a lower R² of 0.968, due to higher irradiance levels and weather fluctuations. The present model captures long-term dependencies in the time series data, significantly improving prediction accuracy compared to conventional methods. This approach offers a cost-effective and practical solution for predicting photovoltaic hydrogen production, demonstrating significant potential for the optimization of the operation of photovoltaic hydrogen production systems in diverse environments.

Off-grid wind–photovoltaic systems can provide low-carbon power for liquid hydrogen production, but renewable generation fluctuations complicate the coordination of fixed liquid hydrogen output and external electric load supply. This study develops a 24 h mixed-integer linear programming model for an off-grid wind–photovoltaic liquid hydrogen production system with fixed daily output. The system includes wind turbines, photovoltaic arrays, an electrolyzer, a high-pressure gaseous hydrogen unit, a hydrogen fuel cell, a Claude-cycle liquefaction unit, and a liquid hydrogen tank. The model guarantees a daily liquid hydrogen output of 5000 kg through a terminal inventory constraint and minimizes unmet external electric load, renewable curtailment, and hydrogen purchase. Case studies with different renewable generation profiles compare output guarantee, load supply, renewable utilization, hydrogen purchase, and storage behavior. The results show that the terminal liquid hydrogen inventory reaches 5000 kg in all feasible cases. For the typical wind–photovoltaic case, the optimized dispatch achieves zero renewable curtailment while maintaining external electric-load supply. The electrolyzer converts surplus renewable power into gaseous hydrogen, and the fuel cell supplies backup electricity during low-renewable periods, with a peak hydrogen consumption rate of 425 kg/h. The Claude-cycle liquefaction unit produces the required net liquid hydrogen output by coordinating liquefaction with gaseous hydrogen storage and recycle flows. These findings confirm that high-pressure gaseous hydrogen storage can buffer renewable power volatility and support stable off-grid liquid hydrogen production.