Predicting Hydrogen Fuel Cell Capacity using Supervised Learning Models

Hydrogen fuel cells have emerged as a promising solution for clean energy, but their effectiveness and reliability depend on the precise prediction of their capacity. This research study investigates into the application of various supervised learning models to forecast hydrogen fuel cell capacity. The findings uncover the distinctive strengths and limitations of each regression model in the context of hydrogen fuel cell capacity prediction. Linear Regression stands out for its simplicity and transparency, offering an easy-to-understand approach. On the other hand, Random Forest Regression and Decision Tree Regression demonstrate a knack for handling non-linear relationships within the data. KNN Regression excels in capturing localized patterns, while Gradient Boosting Regression utilizes ensemble learning to achieve heightened accuracy. SVR Regression exhibits adaptability through various kernel functions, and Logistic Regression proves effective in binary classification tasks. Meanwhile, Polynomial Regression effectively captures potential non-linearity present in the data. This study provides guidance in choosing the best model for certain hydrogen fuel cell capacity prediction scenarios by evaluating the performance of various models across a range of assessment measures, such as mean squared error, mean absolute error, and R-squared values.