Multi-Objective Optimization of Building Energy Performance Using Genetic Algorithms Analysis

This study develops a framework for optimizing building energy performance using multi-objective optimization. The EnergyPlus simulation model was validated using the BESTEST methodology under ASHRAE Standard 140. Monte Carlo simulations and sensitivity analyses quantified the effects of key building parameterssuch as insulation thickness, window properties, and set pointson cooling energy consumption and thermal comfort. Roof insulation thickness was found to be the most influential parameter for reducing energy consumption. A multi-objective optimization approach was applied to address the trade-off between cooling energy demand and CO emissions, constrained by predicted mean vote (PMV) thresholds. The optimization results showed that prioritizing cooling energy reduction led to higher CO emissions, while reducing carbon emissions increased cooling energy use. This study offers insights for balancing energy efficiency, sustainability, and occupant comfort in building.