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.

本代码实现了一套面向电解槽技术的概率预测框架，结合逻辑斯蒂S曲线模型（logistic S-curve model）以预测未来部署规模，并采用随机莱特定律模型（stochastic Wright’s Law model）估算未来资本成本。该框架借助蒙特卡洛模拟（Monte Carlo simulations）显式捕捉增长率、饱和水平与学习效应中的不确定性，可生成透明且可复现的预测结果，与该论文中所述方法保持一致。