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Abstract The transition to carbon-neutral energy has increased the reliance upon renewable sources of energy, e.g., wind power, placing added demands on resilience and stability of the power grid. Wind-to-hydrogen production systems can be a solution for addressing these demands. By converting excess wind energy into hydrogen via electrolysis, these systems can effectively store the intermittent energy generated by wind turbines. This study discusses the application of two time-series prediction models, i.e., long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), in forecasting the energy of wind farms, subsequently used for an assessment of hydrogen production means of a proton exchange membrane (PEM) electrolyzer. Using a dataset comprising wind speed, active power, and wind direction, inputs were normalized, and wind direction was transformed into sine and cosine components to retain circular characteristics. Bi-LSTM demonstrated superior accuracy with lower testing RMSE than LSTM. Integrating wind forecasts with a PEM electrolyzer model, incorporating critical electro-chemical parameters, revealed an optimal efficiency of 63.611% at a membrane thickness of 0.00254 cm and a temperature of 70°C. Bi-LSTM forecasts boosted hydrogen production by 5% to 8% compared to LSTM.
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This content will become publicly available on January 1, 2027
Multivariate CNN-Bi-LSTM temporal production forecasting for distributed wind-to-hydrogen integrated systems with efficient data feature extraction
This paper presents a practical framework that integrates wind speed forecasting with proton exchange membrane (PEM) electrolyzer design to optimize hydrogen production. Due to wind speed fluctuations, excess electrical energy is sometimes produced and left unused. A wind-to-hydrogen system addresses this challenge by converting surplus energy into storable hydrogen using a PEM electrolyzer. The proposed approach employs a multivariate supervisory control and data acquisition (SCADA) dataset and applies a convolutional neural network with bi-directional long short-term memory (CNN-Bi-LSTM) for multivariate wind speed temporal forecasting, enabling more efficient PEM operations. Compared to standard deep learning models, the CNN-Bi-LSTM architecture reduces the root mean square error by 52.5% and the mean absolute error by 56%, thereby enhancing hydrogen production forecasting. Simulation results show that a membrane thickness of 0.0252 mm and an operating temperature of 70% achieve the highest overall PEM efficiency of 63.611%. This study demonstrates the integration of deep learning-based forecasting with electrochemical modeling and SCADA datasets as a novel approach for wind-to-hydrogen production systems.
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- PAR ID:
- 10652866
- Publisher / Repository:
- ELSEVIER
- Date Published:
- Journal Name:
- International Journal of Hydrogen Energy
- Volume:
- 199
- Issue:
- C
- ISSN:
- 0360-3199
- Page Range / eLocation ID:
- 152853
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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