Advisor: Ajla Aksamija

Project Title:  A Framework for Performance-Based Facade Design: Approach for Multi-Objective and Automated Simulation and Optimization

Project Description: Buildings have a considerable impact on the environment, and it is crucial to consider environmental and energy performance in building design. Buildings account for about 40% of the global energy consumption and contribute over 30% of the CO2 emissions. A large proportion of this energy is used for meeting occupants’ thermal comfort in buildings, followed by lighting. The building facade forms a barrier between the exterior and interior environments, therefore it has a crucial role in improving energy efficiency and building performance.

In this regard, decision-makers are required to establish an optimal solution, considering multi-objective problems that are usually competitive and nonlinear, such as energy consumption, financial costs, environmental performance, occupant comfort, etc. Sustainable building design requires considerations of a large number of design variables and multiple, often conflicting objectives, such as the initial construction cost, energy cost, energy consumption and occupant satisfaction. One approach to address these issues is the use of building performance simulations and optimization methods.

This research presents a novel method for improving building facade performance, taking into consideration occupant comfort, energy consumption and energy costs. The research discusses the development of a framework, which is based on multi-objective optimization and uses a genetic algorithm in combination with building performance simulations. The framework utilizes EnergyPlus simulation engine and Python programming to implement optimization algorithm analysis and decision support. The framework enhances the process of performance-based facade design, couples simulation and optimization packages, and provides flexible and fast supplement in facade design process by rapid generation of design alternatives. The paper describes the components and functionality of this framework in detail, as well as two-step optimization technique which is a new technique that combines GA and deep learning. This technique improves the framework speed, performance, and stability of the artificial neural network (ANN) and reduces the sensitivity.