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Bidirectional Gated Recurrent Unit Network for Hydrogen Production Forecasting from Electrolysis-Based Renewable Systems.

Lima Hongou
Volume 5

Abstract

Green hydrogen production through water electrolysis powered by renewable energy sources has emerged as a promising pathway for large-scale decarbonization, long-duration energy storage, sector coupling, and the integration of variable renewable generation into future energy systems. However, hydrogen production from electrolysis-based renewable systems is strongly affected by the intermittent and uncertain nature of solar and wind energy, fluctuations in available electrical power, electrolyzer operating conditions, water supply constraints, temperature variation, efficiency degradation, and dynamic demand for hydrogen storage or utilization. Accurate forecasting of hydrogen production is therefore essential for energy management, electrolyzer scheduling, storage planning, grid balancing, and techno-economic optimization. To address these challenges, this paper proposes a Bidirectional Gated Recurrent Unit (BiGRU) network for hydrogen production forecasting from electrolysis-based renewable energy systems.

The proposed framework is designed to model nonlinear temporal dependencies between renewable power generation and hydrogen output. The input data include historical photovoltaic power, wind power, solar irradiance, wind speed, ambient temperature, electrolyzer input power, stack voltage, stack current, operating temperature, water consumption rate, electrolyzer efficiency, hydrogen flow rate, storage tank pressure, and previous hydrogen production records. A comprehensive preprocessing pipeline is applied to handle missing values, remove outliers, normalize continuous variables, align multi-source time-series measurements, and construct sliding-window input sequences for multi-step forecasting. The framework supports short-term, day-ahead, and multi-horizon hydrogen production forecasting to meet both operational and planning requirements.

The BiGRU architecture is employed to capture temporal dependencies in both forward and backward directions, enabling the model to learn production patterns from preceding system behavior as well as contextual relationships across the entire input sequence. Compared with conventional recurrent neural networks, GRU-based models provide efficient gating mechanisms with fewer parameters than LSTM architectures, making them suitable for renewable hydrogen forecasting applications that require both accuracy and computational efficiency. The bidirectional structure improves the model’s ability to represent complex relationships among renewable generation variability, electrolyzer response dynamics, and storage system conditions. Dropout regularization, batch normalization, adaptive learning-rate scheduling, and hyperparameter optimization are incorporated to improve convergence stability and prevent overfitting.

The proposed model can be evaluated using simulated and experimental datasets from renewable-powered electrolyzer systems, hydrogen microgrids, power-to-gas testbeds, and integrated wind–solar–hydrogen energy systems. Forecasting performance is assessed using Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, normalized RMSE, coefficient of determination, forecasting skill score, and computational latency. Comparative analysis is conducted against persistence models, ARIMA, support vector regression, random forest, XGBoost, standard RNN, LSTM, unidirectional GRU, CNN-GRU, and transformer-based time-series forecasting models. Experimental evaluation is expected to demonstrate that the proposed BiGRU network achieves improved forecasting accuracy, particularly under rapidly changing renewable generation and variable electrolyzer loading conditions.

For practical renewable hydrogen system management, the forecasting outputs can be integrated into energy management systems to support optimal electrolyzer dispatch, hydrogen storage scheduling, battery–hydrogen coordination, grid export decisions, renewable curtailment reduction, and demand-side hydrogen supply planning. In addition, model interpretability is enhanced through temporal attention analysis, feature importance estimation, and sensitivity analysis to identify the variables most strongly influencing hydrogen production, such as renewable input power, electrolyzer efficiency, stack temperature, and storage pressure. These insights can assist operators in improving system reliability, reducing operating costs, and maximizing renewable-to-hydrogen conversion efficiency.

The proposed BiGRU-based forecasting framework provides a scalable, accurate, and computationally efficient solution for hydrogen production prediction in electrolysis-based renewable energy systems. By capturing bidirectional temporal patterns across renewable generation, electrolyzer operation, and storage behavior, the framework can support more reliable green hydrogen production planning, improve utilization of surplus renewable energy, reduce operational uncertainty, and contribute to the development of sustainable hydrogen energy infrastructure

Keywords

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