"LSTM IoT data for Green hydrogen Electrolyzer"

"Green hydrogen production through water electrolysis is an important aspect of the decarbonization of the energy sector. In the current research, a novel predictive framework based on the long short-term memory network has been proposed to predict the hydrogen production rate in alkaline waterelectrolyzers in real-time, considering the tropical climate of Southern India. In the proposed framework, a dual-layer LSTM network has been employed to predict the hydrogen production rate based on eight operational parameters, including dual-zone temperature, dual-stage pressure, flow rate, current, voltage, and purity. The simulation has been employed to train the model over 17,000 iterations through the Adam optimizer. The results showRoot Mean Square Error of 6.06 kg\/s, Mean Absolute Error of 5.19 kg\/s and coefficeinet of determination R2 of 0.874 on the test data. However, this study has limitations in predicting the production rate of the system during rapid transitions. The contributions of this research include the following: First, theresearch provided the first LSTM network framework to predict the hydrogen production rate in alkaline water electrolyzers, which has been validated in the context of tropical climate operations. Second, the results have identified the major difficulties associated with the accuracy of the proposed framework inpredicting the production rate of the system in different regimes. Third, the proposed research has provided important insights into the real-time optimization of the hydrogen production rate in alkaline water electrolyzers. "