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An Initial Approach About Data Preprocessing Techniques Applied to Polymer Electrolyte Fuel Cells: A Case Study

Abstract:

Like other fields, a great amount of data is present when a polymer fuel cell is analyzed. Several variables are involved during fuel cells’ operation, i.e., pressure, temperature, and chemical compounds in the anode and cathode side. A correct data preprocessing allows an appropriate quality of data to evaluate the fuel cell’s behavior during the energy conversion process. It also helps to pbkp_redict a fuel cell’s future performance, considering techniques such as machine learning. This paper aims to provide the pathway to follow when a fuel cell’s collected dataset is used to solve a supervised learning problem such as classification and regression. Therefore, data preprocessing techniques play an essential step before feeding it into any model to learn. It impacts on better learning, generalization, pbkp_rediction, and computational time.