Energy-saving parameterized design of buildings based on genetic algorithm

Purpose: With the problem of environment and energy becoming prominent, energy conservation and emission reduction have received more attention. In the using process, buildings not only have the inherent energy consumption but also have the energy consumption of equipment that is installed for improving the indoor environment. This study aims to investigate how to reduce the energy consumption of buildings through utilizing natural resources. Design/methodology/approach: This paper briefly introduces three objective functions in the building energy-saving model: building energy consumption, natural lighting and natural ventilation. Genetic algorithm was used to optimize the building parameters to achieve energy conservation and comfort improvement. Then a two-story rental building was analyzed. Findings: The genetic algorithm converged to Pareto optimal solution set after 10,000 times of iterations, which took 61024 s. The lowest energy consumption of the scheme that was selected from the 70 optimal solutions was 5580 W/(m2K), the lighting coefficient was 5.56% and Pressure Difference Pascal Hours (PDPH) was 6453 h; compared with the initial building parameters, the building energy consumption reduced by 3.40%, the lighting coefficient increased by 11.65% and PDPH increased by 9.54%. Originality/value: In short, the genetic algorithm can effectively optimize the energy-saving parameters of buildings.