Ventilation systems onboard ships are major contributors to non-propulsion energy use, yet optimizing their operation remains challenging due to fluctuating occupancy, dynamic environmental conditions, and indoor air quality (IAQ) requirements. This study proposes a novel energy optimization framework, integrating Extreme Gradient Boosting (XGBoost) with an improved Chimp Optimization Algorithm (ChOA) to minimize energy consumption while ensuring IAQ compliance. The XGBoost model predicts fan energy and airflow based on operational and environmental conditions. IAQ compliance is assessed against the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) occupancy-based airflow thresholds, with deviations penalized during optimization. ChOA was enhanced using opposition-based learning and adaptive parameter control to improve convergence stability. The framework was validated using real data from summer and winter voyages of a roll-on/roll-off passenger (RoPax) vessel. Results show that XGBoost-ChOA achieved energy savings of 4.9–8.9% in summer and 1.2–2.1% in winter, while maintaining IAQ. Compared to conventional metaheuristic approaches, the method demonstrated more stable convergence. The proposed framework supports voyage-level ventilation planning and enables ship operators to make informed, data-driven energy-saving decisions without compromising regulatory compliance or passenger comfort. • Novel energy optimization framework for ship ventilation systems integrating XGBoost and chimp optimization algorithm (ChOA) • Algorithmic enhancements applied to ChOA to improve convergence performance • Validation of the model’s effectiveness using real ship operational data • Energy consumption reduced by 4.9–8.9% in summer and 1.2–2.1% in winter while maintaining indoor air quality
Okonkwo et al. (Thu,) studied this question.