Energy Reports (Nov 2021)
A comprehensive method for optimizing the design of a regular architectural space to improve building performance
Abstract
Improving building performance is of great significance to protecting the ecology, saving energy and enhancing the living environment. This paper developed a comprehensive method for optimizing the design of a regular architectural space to improve building performance. The research aims to provide architects with robust and accurate design references when conducting design tasks. The entire optimization process is divided into three steps. The first step is to build a database by generating the research objects randomly and performing building simulations on them. The second step is to train artificial neural network (ANN) models as a substitute of the time-consuming building simulation in the multi-objective optimization to predict the building performance quickly. The last step is to perform multi-objective optimization based on the actual design constraints. To demonstrate the optimization process, a common type of classroom space defined by 30 design parameters was selected as a case study. The optimization objectives were set as energy demand, thermal environment and daylight environment. The accuracy of different ANN models was assessed. To imitate realistic design tasks, three design scenarios with constraints are used in the optimization. All the three optimizations finished in 350 s. Compared to the traditional optimization method based on simulation, the optimization calculation is accelerated by approximately 2,570 times. To ensure the reliability of the optimization results, several nondominated solutions of each case were validated by simulation, and there were good agreements between the simulation results and the optimization results. An integrated solution and a reference solution was defined for each case. Compared to the reference solution, the objectives of the integrated solution of the three cases have been improved by 24.6%, 18.7% and 14.2% on average, respectively, indicating that this method is feasible and effective to improve building design in actual tasks.
