Architecture Engineers Award

Accurate prediction of indoor operative temperature is essential for improving HVAC system performance, enhancing occupant comfort, and reducing energy consumption in buildings. Operative temperature represents the combined effect of air temperature and the mean radiant temperature of surrounding surfaces as experienced by occupants. In highly controlled environments such as sentry buildings, precise thermal forecasting enables more responsive and energy-efficient climate control strategies. This study proposes a hybrid deep learning framework to improve the accuracy and robustness of indoor operative temperature prediction. Concept of Operative Temperature and Its Role in Thermal Comfort Operative temperature is widely used as a key indicator of indoor thermal comfort because it integrates both air temperature and radiative heat exchange between occupants and surrounding surfaces. Traditional temperature prediction approaches often focus only on air temperature, overlooking the infl...

Dynamic façade technologies have emerged as effective strategies for improving building energy efficiency while maintaining indoor environmental quality. Thermotropic (TT) glazing is a responsive glazing technology capable of modulating solar heat gain automatically in response to ambient temperature changes. This study investigates whether TT glazing can serve as an effective alternative to conventional external vertical shading devices by examining building energy consumption, daylighting performance, and thermal comfort in office spaces across multiple climatic conditions. Thermotropic Glazing as a Dynamic Solar Control Strategy Thermotropic glazing operates through temperature-responsive materials that adjust their optical properties when exposed to solar radiation and rising surface temperatures. As the glazing becomes less transparent at higher temperatures, solar heat gain is reduced without the need for mechanical control systems. This adaptive behavior allows the glazing to...

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...

This study investigates the application of six machine learning regression models to predict building performance in a residential unit located in Sari, Iran. Using a calibrated EnergyPlus model and three years of utility data, the research evaluates primary energy consumption, emissions, indoor air quality, thermal comfort, and visual discomfort. The aim is to enhance sustainable design decision-making by comparing the efficiency and accuracy of different models. Machine Learning Models in Building Performance The research evaluates Random Forest, K-Nearest Neighbors, Support Vector Regression, Artificial Neural Network, Extreme Gradient Boosting, and Linear Regression. Each model is tested against real-world performance indicators, highlighting their predictive strength and weaknesses in handling multidimensional building datasets. Dataset and Methodology A synthetic dataset of 1,826 configurations with 25 input variables was developed using EnergyPlus. The dataset was split in...