Study on Stochastic and Autoregressive Time Series Forecasting for Hydrogen Refueling Station

Hydrogen refueling stations are pivotal for renewable energy and carbon neutrality; however, they encounter challenges owing to equipment malfunctions. This study addresses the use of time-series forecasting techniques to predict and diagnose critical equipment failure at these stations. An analysis of the station equipment was conducted to create scenarios for potential malfunctions in compression equipment. Techniques such as the Recurrent Neural Network (RNN), Long Short-Term Memory network (LSTM), and Gated Recurrent Unit (GRU) have been employed to forecast the conditions of high-pressure compression equipment. Deep neural networks were constructed to enhance prediction accuracy, typically achieving an error margin of 0.01. Multi-step predictions using autoregression were utilized to bolster equipment resilience against aging and progressive failures. Autoregressive prediction models, particularly those using LSTMs and GRUs, outperform RNNs. However, predictions may be subject to errors due to algorithmic limitations and environmental factors. This study introduces a stochastic forecasting approach that, utilizes Gaussian distributions to predict probability distributions, not single-point estimates. This method yielded a 95% prediction interval with a standard deviation of 1.96. The reliability of multi-time step forecasts is significantly improved by adopting stochastic autoregressive forecasting and establishing prediction intervals. The proposed model enhances not only the accuracy of equipment failure predictions but also proactive maintenance, thus reducing downtime and boosting the efficiency of the hydrogen fuel infrastructure, which contributes to the wider utilization of hydrogen as a clean energy source.