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 ° C 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. • A 1D CNN model is developed to enhance data feature extraction. • A CNN-Bi-LSTM model forecasts integrated wind-to-hydrogen system production. • Achieves 52.5% RMSE reduction compared to standard wind speed forecasting models. • Integrated CNN-Bi-LSTM with PEM electrolyzer for hydrogen optimization.