Toward sustainable energy production: a comparative machine learning framework for predicting green hydrogen cost across the african continent

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers can adopt the machine learning benchmarking approach to improve cost prediction models for hydrogen or other energy technologies.

🏢実務担当者:Practitioners in hydrogen project development can use the framework for quick cross-country cost screening before detailed feasibility studies.

🏛政策担当者:Policymakers can leverage the SDG-linked cost drivers to prioritize green hydrogen investments aligned with energy security and climate goals.

Rapid decarbonisation of hard-to-electrify sectors requires low-emission hydrogen, but deployment is constrained by uncertainty in the Levelized Cost of Hydrogen (LCOH) across diverse national contexts. Using Africa as a case study, where green hydrogen planning spans highly heterogeneous conditions, this study develops a comparative machine learning framework for country-scale cost screening before major infrastructure commitments. A harmonized dataset of 54 African scenarios was compiled, with LCOH (EUR/kg) as the target variable and 14 predictors capturing project scale, renewable capacity, storage and transport infrastructure, investment and maturity stage, energy security and sustainability indices, market variables, CO2 reduction potential, and water demand. The workflow integrated exploratory statistics, preprocessing, and systematic benchmarking of 11 regression models using an independent 20% holdout split, complemented by repeated nested cross-validation. Across the compiled cases, LCOH ranged from 3.75 to 5.60 EUR/kg with a median of 4.90 EUR/kg, showing clear cost stratification by project maturity stage. Holdout validation identified Hyperopt optimized Gradient Boosting as the best performing model (R2 = 0.9762, RMSE = 0.0840 EUR/kg, MAE = 0.0663 EUR/kg), followed closely by Bayesian tuned XGBoost (R2 = 0.9713). Nested cross-validation confirmed model stability (Hyperopt_GB: R2 = 0.9710 ± 0.032). SHAP analysis revealed that renewable energy capacity, electrolyser capacity, and the energy security index contributed most to predicted LCOH variability within the dataset. The framework provides a transferable screening pipeline for prioritizing investment and data collection in data-scarce settings, with explicit linkages to Sustainable Development Goal (SDG) relevant indicators, including energy security, climate mitigation, and water stress. This approach complements deterministic techno-economic appraisal by enabling rapid cross-country comparison during early-stage planning Fig.S1. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-026-47726-w.

• semanticscholar https://doi.org/10.1038/s41598-026-47726-wfirst seen 2026-05-15 19:27:42