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<p>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, CO<sub>2</sub> 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 (R<sup>2</sup> = 0.9762, RMSE = 0.0840 EUR/kg, MAE = 0.0663 EUR/kg), followed closely by Bayesian tuned XGBoost (R<sup>2</sup> = 0.9713). Nested cross-validation confirmed model stability (Hyperopt_GB: R<sup>2</sup> = 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.<InternalRef RefID="MOESM2">S1</InternalRef>.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月17日

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  • Ashraf M. T. Elewa,
  • Moustafa Gamal Snousy,
  • Ahmed M. Saqr,
  • Hussein M. Elshafie,
  • Ashraf R. Abouelmagd,
  • Ali Mahmoud Hussain,
  • Tarek Abd El-Hafeez

摘要

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.