Research Project Overview:

This research project focuses on the development of  an automated framework that couples’ energy simulations and Artificial Intelligence (AI) driven methods for multi-objective optimization of building facade design options. The optimization process considers three key performance metrics: energy use, occupant comfort and energy costs. The project utilizes research methods such as mixed-method literature review, building energy simulations, multi-objective optimization and machine learning. The  framework provides an automated and efficient approach for performance-based facade design, enabling decision-makers to rapidly evaluate facade alternatives and make informed design choices that balance energy efficiency, occupant comfort, and cost-effectiveness during early design stages. Effective facade design is complex, and requires simultaneous consideration of multiple performance objectives including energy performance, occupants’ comfort and operational cost. In response to these challenges, design decision-makers require reliable tools and methods to evaluate and optimize building facade design for energy efficiency and occupant comfort. The project will ultimately result in development of a new computational tool, which can be used for rapid, performance-based facade design exploration and decision-making.

Publications:

Aghimien, E., and Aksamija, A., (2026). “Simulation-Driven XGBoost Model for Multi-Output Building Facade Performance Prediction”, Proceedings of the 2026 Annual Modeling and Simulation Conference (ANNSIM), Orlando, Florida, May 4-7.