Theoretical Potential of Green Hydrogen Production in Southern Morocco: Multi-Horizon Forecasting Using Machine Learning

Abstract

This study investigates multi-horizon forecasting of solar-based green hydrogen (H2) production in Southern Morocco (Dakhla: 23.7° N, 15.9° W; Laâyoune: 27.2° N, 13.2° W; and Guelmim: 29.0° N, 10.1° W) using daily meteorological time series (2010–2025) derived from NASA POWER and PVGIS. Daily H2 production (kg/day) is estimated through a PV-to-hydrogen conversion model assuming a 100 MW PV plant, a performance ratio of 0.75, and a specific electricity consumption of 50 kWh/kg-H2. We formulate a supervised learning problem to predict H2 at multiple horizons (J + 1, J + 3, and J + 7), combining calendar features, physically motivated variables, and lagged/rolling statistics. Models are trained on 2010–2023 and evaluated on 2024–2025 using R2, RMSE, and sMAPE. CatBoost, Random Forest, and LSTM are compared; additionally, a physically interpretable two-step framework is proposed. For J + 1, the best results reach R2 values of 0.863 in Dakhla, 0.795 in Laâyoune, and 0.669 in Guelmim. At the J + 7 horizon, predictive performance remains robust with R2 values of 0.792, 0.759, and 0.556, respectively. The proposed two-step approach yields comparable accuracy (e.g., Dakhla J + 1 R2 = 0.862) while improving physical consistency.