Architecture Engineers Award

In the global pursuit of carbon neutrality , improving the operational energy efficiency of buildings has become a central challenge for the architecture, engineering, and construction sectors. Advances in Artificial Intelligence (AI) and Digital Twin technologies offer new opportunities to optimize building performance through data-driven decision-making. This research investigates the role of AI-driven Digital Twins in building operations, positioning them as an effective approach for enhancing sustainability, reducing energy consumption, and supporting intelligent control strategies. Design Science Research Framework The study adopts a Design Science Research (DSR) methodology to systematically guide the development, implementation, and evaluation of a digital artifact for energy-efficient building operation. DSR enables the structured creation of a practical solution while ensuring theoretical rigor through iterative problem identification, artifact design, demonstration, and eva...

Traditional building energy simulation models often overlook the stochastic behavior of occupants and the complex interactions between multiple devices. This limitation creates a significant gap between predicted and actual building energy consumption. Addressing this issue requires behavior-sensitive frameworks that integrate both human and technical dimensions of building performance. The proposed study bridges this gap by introducing a fuzzy multi-criteria decision-making (FMCDM) approach coupled with evolutionary optimization to ensure realistic and adaptive performance predictions. Significance of Occupant Behavior in Energy Modeling Occupant behavior is one of the most influential yet uncertain factors in determining building energy performance. Stochastic patterns, such as irregular use of appliances, varying thermal preferences, and diverse daily routines, make deterministic models insufficient. Incorporating behavioral diversity through FMCDM provides more accurate results, ...