Performance-based Facade Design Tool: Approach for Automated and Multi- Objective Simulation and Optimization
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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 to 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, decisionmakers 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 many 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 development of a framework, which is based on multi-objective optimization and uses a Genetic Algorithm (GA) and machine learning in combination with building performance simulations. The framework utilizes the EnergyPlus simulation engine and custom scripts using Python programming to implement optimization algorithm analysis and decision support. The framework is automated in all steps: generating design scenarios, sending scenarios to the simulator, collecting the specific output, and decision making in optimization phase. So, the framework enhances the process of performance-based facade design, couples simulation and optimization packages, and provides a flexible and fast supplement in the facade design process by rapid generation of design alternatives. The study describes the components and functionality of this framework in detail, as well as a two-step optimization technique, which is a new technique that combines GA and Machine Learning. This technique improves the framework speed, performance, and stability of an artificial neural network (ANN) and reduces the sensitivity. The case study for a test cell presents, illustrating how the framework is used to test a variety of design possibilities and validation of this framework, as well as its application for facade design in different climates.