A multi-objective energy retrofit optimization framework for enhancing sustainability in industrial buildings

The building sector accounts for approximately 30–40% of global energy consumption, with industrial buildings face particular sustainability challenges due to their large floor areas and the need for thermally comfortable conditions for extensive workforces. Energy retrofitting presents a cost-effective and environmentally sustainable strategy to address these challenges. This study proposes a multi-objective optimization framework that simultaneously minimizes primary energy consumption, thermal discomfort, and life cycle cost by integrating EnergyPlus simulation with a MATLAB®-based Particle Swarm Optimization (PSO) algorithm. The framework is applied to a stone processing plant, evaluating a broad set of energy efficiency measures, including building envelope upgrades, HVAC system improvements, maintenance programs, and modified operation schedules. The optimized solutions achieved annual energy savings of up to 85,855 kWh, equivalent to approximately 39 kWh/m2, reduced lifecycle cost by up to 9% over a 20-year period and decreased thermal discomfort hours from 51% in the baseline case to 15% in the best-performing comfort-oriented solutions, corresponding to a 36 percentage-point reduction, or approximately a 71% relative reduction compared with the baseline. Pearson and Spearman correlation analyses further revealed key interdependencies among the sustainability metrics. The proposed framework provides a scalable and replicable approach for advancing sustainability in industrial building retrofits.