Green hydrogen production ensemble forecasting based on hybrid dynamic optimization algorithm

Abstract

Solar-powered water electrolysis can produce clean hydrogen for sustainable energy systems. Accurate solar energy generation forecasts are necessary for system operation and planning. Al-Biruni Earth Radius (BER) and Particle Swarm Optimization (PSO) are used in this paper to ensemble forecast solar hydrogen generation. The suggested method optimizes the dynamic hyperparameters of the deep learning model of recurrent neural network (RNN) using the BER metaheuristic search optimization algorithm and PSO algorithm. We used data from the HI-SEAS weather station in Hawaii for 4 months (September through December 2016). We will forecast the level of solar energy production next season in our simulations and compare our results to those of other forecasting approaches. Regarding accuracy, resilience, and computational economy, the results show that the BER-PSO-RNN algorithm has great potential as a useful tool for ensemble forecasting of solar hydrogen generation, which has important ramifications for the planning and execution of such systems. The accuracy of the proposed algorithm is confirmed by two statistical analysis tests, such as Wilcoxon’s rank-sum and one-way analysis of variance (ANOVA). With the use of the proposed BER-PSO-RNN algorithm that excels in processing and forecasting time-series data, we discovered that with the proposed algorithm, the Solar System could produce, on average, 0.622 kg/day of hydrogen during the season in comparison with other algorithms.