Multi-objective optimization of building energy consumption and thermal comfort based on SVR-NSGA-II
Ailing Wang, Ying Xiao, Chunlu Liu, Zhao Ying, Shaonan Sun
Abstract
It is necessary to reduce the huge energy consumption of buildings while ensuring indoor thermal comfort. The building envelope design notably affects the energy consumption and indoor thermal comfort. This study proposes a framework of support vector regression non-dominated sorting genetic algorithm-II (SVR-NSGA-II) to study the multi-objective optimization of buildings and discover the best building envelope design. First, the data are obtained by OpenStudio 1.1.0 simulation. Then SVR is used to describe the relationships between the parameters and the two objectives. Using SVR models as the objective functions, NSGA-II is applied to optimize the objectives. Finally, taking a proposed office building as an example, the developed framework is applied, verifying its feasibility and effectiveness. The optimized parameters of the building envelope are as follows: the exterior wall U-value, roof U-value, exterior wall U-value, solar heat gain coefficient value (SHGC), and south, north, east, west window-to-wall ratios are respectively equal to 0.664 W / ( m 2 ⋅ K ) , 0.393 W / ( m 2 ⋅ K ) , 1 W / ( m 2 ⋅ K ) , 0.381, 0.697, 0.7, 0.273, and 0.254. The design of the proposed building can be modified to provide a more energy efficient and comfortable building. The proposed model saves time and simplifies operation compared with traditional building information modeling methods.