Code underlying research on: forecast CAPEX and deployment of electrolysers (AEC & PEM)
Datacite citation style
van Eijden, Bram (2025): Code underlying research on: forecast CAPEX and deployment of electrolysers (AEC & PEM). Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/29952e47-4482-47ba-aa6f-510417bff0d0.v1
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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, published, posted
Publisher
4TU.ResearchDataFormat
script/.py spreadsheet/.xlsxOrganizations
TU Delft, Faculty of Technology, Policy and Management, Complex Systems Engineering and ManagementDATA
Files (8)
- 4,082 bytesMD5:
079e443bc04989006d109f113a27fbb8README.txt - 8,496 bytesMD5:
098938a0602b2ce788b8e0791d41ce30CAPEX AEC.py - 9,001 bytesMD5:
d270d2053988a6f61b3eeca07d1a589aCAPEX PEM.py - 7,013 bytesMD5:
d4d73b46a871160d7a2ec6917b45942dDeployment AEC.py - 5,926 bytesMD5:
2a04713c180c0dbcf41bc310f48eeb82DEPLOYMENT FORECAST AEC.xlsx - 9,265 bytesMD5:
d4b756713f83daf985d37ba9e4598686DEPLOYMENT FORECAST PEM.xlsx - 6,370 bytesMD5:
c6ab90c5ead7c7214710c55a8b27e80cDeployment PEM.py - 1,071 bytesMD5:
8fb8ccc7f91ad0785d4b8bc3eb78f652LICENSE.txt -
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