An Optimized Data Analysis on a Real-Time Application of PEM Fuel Cell Design by Using Machine Learning Algorithms

In recent years, machine learning algorithms have been applied in many real-time applications. Crises the energy sector are primary challenges experienced today among all countries across globe, regardless of their economic status. There is a huge demand to acquire and produce environmentally friendly renewable distribute utilize it efficiently because its production cost. PEMFC known for efficiency comparatively low cost, can be an alternative source. The these still enhanced with help advanced technologies like artificial intelligence, as they provide optimal solution explore hidden knowledge from generated data. proposed model attempts compare several design techniques varied humidity levels. To enhance performance PEMFC, various humidification processes were considered during experimental study. reduces heat generation increases PEM fuel cell. levels such 100%, 50%, 10% tested models. SVMR, LR, KNN observed RMSE value evaluation parameters. results show that SVMR has rate 0.0046, LR method 0.0034, 0.004. analysis shows provides better accuracy than other enhances performance.