Forecasting Hydrogen Vehicle Refuelling for Sustainable Transportation: A Light Gradient-Boosting Machine Model

Abstract

Efficiently predicting and understanding refuelling patterns in the context of HFVs is paramount for optimising fuelling processes, infrastructure planning, and facilitating vehicle operation. This study evaluates several supervised machine learning methodologies for predicting the refuelling behaviour of HFVs. The LightGBM model emerged as the most effective predictive model due to its ability to handle time series and seasonal data. The selected model integrates various input variables, encompassing refuelling metrics, day of the week, and weather conditions (e.g., temperature, precipitation), to capture intricate patterns and relationships within the data set. Empirical testing and validation against real-world refuelling data underscore the efficacy of the LightGBM model, demonstrating a minimal deviation from actual data given limited data and thereby showcasing its potential to offer valuable insights to fuelling station operators, vehicle manufacturers, and policymakers. Overall, this study highlights the potential of sustainable predictive modelling for optimising fuelling processes, infrastructure planning, and facilitating vehicle operation in the context of HFVs.