Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study

Abstract

In recent years, with the progress of computer technology, artificial intelligence has been rapidly developed and begun to be applied in industry, economy and other aspects. Besides, with the pursuit of green hydrogen, hydrogen-based hybrid renewable energy systems have become the focus of the development of the hydrogen industry. This paper compares different artificial intelligence applications in hydrogen-based hybrid renewable energy systems and carries out a case study in a typical area. Firstly, this paper summarizes important works in literature, which use artificial intelligence methods to predict the supply chain of the renewable energy system, including the prediction of renewable energy system resources, output power, load demand and terminal electricity price. Secondly, main articles about artificial intelligence optimization algorithms used in renewable energy systems are also summarized, including swarm and non-swarm biological heuristics, physical or chemical heuristics and hybrid optimization algorithms. Finally, a case study is carried out in Tikanlik, Xinjiang, China. Tikanlik’s weather and load data train the artificial neural network to predict system output power. It shows that 99.32% of the relative error of the test set is less than 3%, which proves that this model can achieve good prediction results.

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

“…Targeted investigations pertain to renewable energies systems [ 10 ], hydrogen-based hybrid renewable energy systems [ 113 ], photovoltaic power generation forecasting [ 114 ], energy management systems [ 115 ], energy demand side [ 116 ], waste management [ 117 , 118 ], sustainable transportation development [ 119 ], ecosystem services [ 98 ], biodiversity protection [ 120 ], urban water resource management [ 99 , 121 ], air pollution [ 122 ], flood resistance [ 123 ], flood risk assessment [ 124 ], and flood prediction [ 16 ]. Important to note is that the energy sector is a primary user of AI in smart cities [ 8 , 125 ], with various technologies supporting the monitoring, analysis, and application of planning processes to combat pollution in urban environment.…”