Code underlying research on: forecast CAPEX and deployment of electrolysers (AEC & PEM)

This code implements a probabilistic forecasting framework for electrolyser technologies, combining a logistic S-curve model to project future deployment and a stochastic Wright’s Law model to estimate future capital costs. It uses Monte Carlo simulations to explicitly capture uncertainty in growth rates, saturation levels, and learning effects, providing transparent and reproducible projections aligned with the methods described in the thesis.