Abstract
The contradiction between the indoor environment and building energy consumption has been controversial. The design of building envelope involves many parameters such as window size and exterior wall material. These parameters have significant influence on building energy-saving design and indoor environment. In this paper, nondominant sorting genetic algorithm-II (NSGA-II) is utilized to calculate winter heat consumption, indoor total lighting energy consumption, and thermal comfort. The Pareto method is used to select the compromise solution and effective value of each building parameter. Different from other studies, we add more architectural design variables into the model calculation, which can bring architects more detailed energy-saving design content.
Highlights
With global energy shortages, many countries have adopted corresponding energy policies, and global energy intensity declined by 1.8 percent in 2016, based on primary energy demand for gross domestic product (GDP)
Indoor thermal performance, thermal comfort, and lighting energy consumption of nondominant sorting genetic algorithm-II (NSGA-II) are optimized by building geometry and physical boundary
NSGA-II needs to save two quantities during sorting: (i) e number of dominant np: this amount is the number of all individuals who can dictate the individual p in the feasible solution space (ii) e controlled individuals gathered Sp: this quantity is a collection of all individuals in the feasible solution space that is dominated by individual p
Summary
Many countries have adopted corresponding energy policies, and global energy intensity declined by 1.8 percent in 2016, based on primary energy demand for gross domestic product (GDP). Indoor thermal performance, thermal comfort, and lighting energy consumption of NSGA-II are optimized by building geometry and physical boundary. Indoor basic thermal comfort, and indoor use lighting are the three objectives of this study. E second (f2 ) is to calculate the total lighting energy consumption f2 for the whole year, and we use the PMV (predicted average voting) value f3 to analyze the thermal comfort of the room in summer. Let the distance from the observation point to the left wall be x, which is calculated as follows: when x ≤ D/2 − Li/2, D
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