Digital Twin Based Evolutionary Building Facility Control Optimization

This work addresses a real-world building facility control problem by using evolutionary algorithms. The variables are facility control parameters, such as the start/stop time of air-conditioning, lighting, and ventilation operation, etc. The problem has six objectives: annual energy consumption, elec-tricity cost, overall satisfaction, thermal satisfaction, indoor air quality satisfaction, and lighting satisfaction. The problem has five constraints: power consumption, temperature, humidity, $\mathbf{CO}_{2}$ concentration, and average illuminance. To solve the problem, we utilize IBEA framework. For efficient solution generation, we employ the steady-state model for IBEA. We propose the total constraint win-loss rank for multiple constraints to treat multiple constraints equally. Experimental results on artificial test problems and building facility control problems show that the proposed constraint IBEA with steady-state and total con-straint win-loss rank archives better search performance than conventional representative algorithms.