Development of an EnergyPlus calibration tool for automated multi-objective calibration of building energy models using a hybrid enhanced NSGA-II algorithm

Accurate calibration of building energy models is essential for reliable prediction of thermal and energy performance. However, existing approaches often require extensive manual effort, including modification of EnergyPlus input files, custom scripting for measured data integration, and programming expertise for performance metric calculations. These challenges become more pronounced in multi-objective calibration problems involving multiple variables (e.g. operative temperature and energy consumption) or multiple rooms, where interdependent objectives increase complexity. This study introduces an automated calibration tool that enables users to upload measured data, select EnergyPlus outputs, and directly evaluate model accuracy using RMSE and NMBE without programming. The tool also integrates a new Hybrid Enhanced NSGA-II algorithm with EnergyPlus to simultaneously minimize RMSE across multiple objectives. HE-NSGA-II enhances standard NSGA-II through Halton initialization, hybrid genetic operators, and elitism and stagnation-driven diversity injection. Across validation scenarios, HE-NSGA-II achieves 36–42% fewer generations, 66–69% runtime saving, and 59–67% improved RMSE.