Extreme gradient boosting (XGBoost) prediction of latent heat storage in Conical‑Fin solar thermal units with Graphene-Alumina Hybrid-Nano‑Additives

Key Points

• Latent heat storage is significantly predicted using extreme gradient boosting techniques, showing marked efficiency.
• The prediction model incorporates graphene-alumina hybrid-nano-additives, showcasing a novel approach to thermal management.
• Assessment using extreme gradient boosting demonstrates improved energy performance in advancing solar thermal systems.
• Enhanced predictions may lead to more efficient designs, highlighting the need for further validation in real-world applications.