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

DOI:10.4121/29952e47-4482-47ba-aa6f-510417bff0d0.v2
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/29952e47-4482-47ba-aa6f-510417bff0d0

Datacite citation style

van Eijden, Bram (2025): Code underlying research on: forecast CAPEX and deployment of electrolysers (AEC & PEM). Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/29952e47-4482-47ba-aa6f-510417bff0d0.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Version 2 - 2025-07-07 (latest)
Version 1 - 2025-07-04

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.

History

  • 2025-07-04 first online
  • 2025-07-07 published, posted

Publisher

4TU.ResearchData

Format

script/.py spreadsheet/.xlsx

Organizations

TU Delft, Faculty of Technology, Policy and Management, Complex Systems Engineering and Management

DATA

Files (13)