The strife for energy efficiency, along with enhancing comfort, has led to the usage of innovative technologies and sustainable development in construction and architecture. One of the most advanced types is digital twin (DT) technology, which provides a virtual dynamic representation of real buildings with current performance and predictive real-time simulation and analysis. Also, robust optimization techniques and evolutionary algorithms (EAs) allow for structural, multi-objective control systems like those in green buildings. Even though there are advancements in sophisticated building and automation control systems (BMS), numerous automation strategies are rigid and deep- rooted in outdated models that don't adapt to automation or dynamic condition changes such as variability in occupancy, weather conditions, and operational deviations. This paper proposes a new system model that mitigates those outlined issues and these inefficiencies by combining a digital twin with an evolutionary optimizing engine. The digital twin simulates building performance while synchronizing with real-time sensor data; simultaneously, the evolutionary model develops optimal control strategies for HVAC, lighting, and ventilation control systems under changing conditions. A case study of a medium-sized commercial building was used, with simulation outcomes showing a 15-25% reduction in energy usage while demonstrating PMV-enhanced thermal comfort (in the range of ±0.5) and responsiveness by the adaptive system. This has resulted in energy-resilient and efficient operations and automation of building systems.
Marangunic et al. (Wed,) studied this question.