Knowledge-Based Modeling Approach: A Schematic Design of Artificial Intelligence of Things (AIoT) for Hydrogen Energy System

The introduction of Artificial Intelligence of Things (AIoT) into hydrogen energy systems causes a paradigm shift in the realm of clean and sustainable energy solutions. The knowledge-based modeling technique described in this research aims to conceptualize and construct an AIoT framework specifically for hydrogen energy systems. The combination of AI and IoT technologies has enormous promise for improving the effectiveness, security, and adaptability of the production, storage, distribution, and use of hydrogen energy. In this paper, we present a systematic technique for building a comprehensive knowledge-based model by integrating domain knowledge, data-driven insights, and machine-learning methods. This model serves as the basis for the creation of an AIoT architecture that seamlessly incorporates predictive capabilities, real-time data analytics, and intelligent decision-making into the complex structure of hydrogen energy systems. The suggested strategy takes into account the particular difficulties offered by hydrogen, such as safety concerns, energy efficiency, and integration with renewable energy sources. The designed AIoT framework shows its potential to optimize hydrogen production processes, predict maintenance needs, ensure safe storage and transportation, and support the integration of hydrogen with existing energy infrastructures by utilizing historical data, real-time sensor inputs, and advanced AI algorithms. The knowledge-based paradigm also encourages flexibility, and scalability, enabling the AIoT system to develop in tandem with scientific breakthroughs and shifting energy needs and adding to the larger discussion surrounding the junction of artificial intelligence, the Internet of Things, and sustainable energy technologies as the globe looks out for novel solutions to address energy concerns.