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.
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). China's energy intensity has declined sharply, reflecting the continuing impact on energy efficiency policies [
The highest potential for green building energy-saving design lies in the renovation of building envelope structures to reduce the use of air conditioning system. Effective design scheme can not only respond to the needs of green energy-saving and long-term economic benefits brought by government investment but also meet the user’s comfort level. Through the local climatic conditions and site conditions, architects can maximize the control of architectural design and construction techniques [
In this article, indoor thermal performance, thermal comfort, and lighting energy consumption of NSGA-II are optimized by building geometry and physical boundary. Building energy consumption, indoor basic thermal comfort, and indoor use lighting are the three objectives of this study. The value range of each building parameter (design variable) is taken according to the national standard specification. The Pareto method selects the optimal solution set, and the different nondominated solutions correspond to discrete building parameters.
We have built three objective functions. The first objective function (
The assumption of diffuse reflection lighting conditions leads to another metric commonly referred to as the daylight factor (DF) [
About complex design calculations, ERC can be determined by multiplying the external obstacle (
In order to achieve a more practical design, additional calibration was performed in this paper, taking into account the maintenance factor (
For the case where the calculation point is close enough to daylight, SC can be considered as the most important component of the three. SC is pure geometric calculation as shown in Figure
Diagram of the calculation of U from the vertical, effective daylight opening ABCD to D (from [
In the lighting design, there are roughly two possibilities for the position of the window relative to the calculation point; this paper refers to the position of the calculation point by Mangkuto [ when when
Sectional view of the window wall, two possibilities for the position of the window relative to the calculated point (m).
Annual lighting energy demand (
The calculation of the basic heat consumption of the envelope structure refers to the national standard [
The heat consumption of the enclosure consists of basic heat consumption (
Indoor design temperature of air conditioning in the area where personnel stay for a long time
Working condition of the category | Thermal comfort level | Interior design temperature (°C) |
---|---|---|
Heating conditions | I level | 22∼24 |
II level | 18∼22 | |
Cooling conditions | I level | 24∼26 |
II level | 26∼28 |
In formula (
Heat transfer coefficient of inner surface of envelope
Internal surface characteristics of envelope |
|
---|---|
A wall, floor or ceiling with a flat surface or ribbed projection, |
8.7 |
A ribbed, well-shaped roof, |
8.1 |
A ceiling with ribbed projections, |
7.6 |
A roof with a well-shaped projection |
7.0 |
Heat transfer coefficient of outer surface of envelope
Outer surface characteristics of envelope |
|
---|---|
Exterior walls and roof | 23 |
A floor above an unheated basement that communicates with outdoor air | 17 |
A floor slab above a nonheated basement with a covered roof and windows on the exterior wall | 12 |
A windowless, unheated floor above a basement | 6 |
The heat transfer process of the building envelope is a very complex process involving convection, conduction, and radiation. At the same time, the outdoor air temperature and heat radiation density also change greatly with the change of outdoor time, and the indoor air temperature also changes. In this paper, the heat transfer equation of external thermal mass and internal air is used to calculate the hourly cooling load of the enclosure. The following assumptions are made: The air distribution within the building, the temperature distribution of the internal thermal mass, and the internal surface temperature of the external thermal mass are balanced. All thermal gain and heat generation in the building are assembled into a heat source The direct thermal solar radiation gain and permeability thermal gain through the opening were ignored. During the nonworking period, the air conditioning system is closed and night ventilation mode is adopted. The ventilation rate is
Based on the above assumptions, the heat balance equations of the inner surface temperature and the indoor air temperature of the outer envelope structure are as shown in equations (
The calculation of outdoor air temperature in summer is similar to formula (
Substitute equation (
Let
Substitute
The general solution of equation (
Substitute equation (
In equation (
PMV is the most comprehensive evaluation considering many factors of human thermal comfort. Table
Thermal ruler of ASHRAE corresponding to PMV value.
Thermal sensation | Cold | Cool | Coolness | Moderation | Microwarm | Warm | Hot |
---|---|---|---|---|---|---|---|
PMV | −3 | −2 | −1 | 0 | +1 | +2 | +2 |
There are coupling effects among various factors affecting thermal sensation, for example, the increase of temperature can be compensated by the decrease of humidity or the increase of wind speed. ASHRAE defines thermal comfort as a psychologically satisfying thermal environment, which mainly includes six major factors, namely, air temperature, average radiant temperature, relative humidity, air velocity, human metabolism, and clothing [
The molecule of formula (
According to ISO-7730 [
The proposed library is located in Chengdu, Sichuan province, with the coordinates of 30.67° north latitude and 104.02° east longitude. The building floor is the sixth floor, and the model verification object in this paper is the standard floor. Figure
3D view of the library and reading room plan.
The remaining initial input data.
Initial input data | |
---|---|
|
12000 |
|
0.1 |
|
0.15 |
|
1 |
|
8.7 |
|
23 |
|
1.2 |
|
2.2 |
|
31 |
|
534 |
|
116 |
|
0.0575 |
|
0.98 |
|
4 |
|
0.1 |
|
1.05 |
|
2.7 |
|
3 |
|
5.5 |
|
3 |
|
1.2 |
|
−2.39 |
|
0.2 |
|
33.5 |
|
400 |
|
39 |
What needs to be explained here is that the value of constant
Spatial profile of calculation points (m).
We set a total of 20 design variables, which will provide architects with more abundant energy-saving building design parameters. The value range of these variables meets the national design standards [
The remaining initial input data.
Variable | Symbol | Minimum | Maximum |
---|---|---|---|
Window height (m) |
|
2 | 4 |
Window width (m) |
|
10 | 18.25 |
Thickness of concrete (m) |
|
0.2 | 0.3 |
Thickness of mortar (m) |
|
0.02 | 0.06 |
The thickness of insulating material (m) |
|
0.01 | 0.06 |
The thickness of the window pane (m) |
|
0.003 | 0.012 |
Heat transfer coefficient of concrete |
|
0.1 | 1.74 |
Heat transfer coefficient of the whole window with closed space and window frame area accounting for 20% |
|
1.07 | 5.8 |
Heat transfer coefficient of concrete |
|
0.76 | 0.93 |
Heat transfer coefficient of insulating material |
|
0.025 | 0.08 |
Set indoor temperature in summer (°C) |
|
24 | 28 |
Floor reflection ratio |
|
0.1 | 0.5 |
Wall reflection ratio |
|
0.3 | 0.8 |
The reflection ratio of window glass |
|
0.07 | 0.84 |
Ceiling reflection ratio |
|
0.6 | 0.9 |
The total transmittance of the window |
|
0.4 | 0.84 |
Density of concrete |
|
300 | 2500 |
Density of mortar |
|
300 | 1800 |
The density of insulating material |
|
20 | 180 |
Set the indoor temperature in winter (°C) |
|
18 | 24 |
NSGA-II is based on the evolution of the “individual” group. When the algorithm performs nondominated sorting, each individual in the The number of dominant The controlled individuals gathered
In this paper, a total of three objective functions are set, which are in conflict with each other and cannot be compared. It is impossible to obtain the global maximum or minimum like single-objective problems. According to the objective function (fitness function) proposed above, NSGA-II is used to obtain a compromise solution set (nondominant solution) with initial input data and design variables, which does not favour any objective function. Its flow is shown in Figure
Flowchart of NSGA-II.
The optimizer was drawn up and run by Python 3.7 with a crossover probability of 0.8, a mutation probability of 1/20, an overall size of 100, and a maximum number of iterations of 200 generations.
It should be reiterated here that
In this process,
According to the calculation result of the above case, for the convenience of the architect to more quickly get detailed building energy efficiency design data, we propose that when two different choices are adopted in the minimum value at 11 moments, the output value of each building parameter is as shown in Tables
The value of each design variable when
Parameters of the category | The values |
---|---|
The height of the window (m) | 3.38 |
The width of the window (m) | 12.48 |
Thickness of concrete (m) | 0.3 |
Heat transfer coefficient of concrete | 0.1 |
Thickness of mortar (m) | 0.06 |
Heat transfer coefficient of mortar | 0.89 |
The thickness of insulating material (m) | 0.06 |
Heat transfer coefficient of insulating material | 0.03 |
The thickness of the window pane (m) | — |
Heat transfer coefficient of window | 5.8 |
Set indoor temperature in summer (°C) | 28 |
The reflection ratio of the floor | 0.1 |
The reflection ratio of the wall | 0.3 |
Window reflection ratio | 0.84 |
Ceiling reflection ratio | 0.9 |
The total transmittance of the window | 0.74 |
Density of concrete ( |
1533 |
Density of mortar ( |
1799 |
Density of insulating material ( |
132 |
Set indoor temperature in winter (°C) | 19 |
The value of each design variable when
Parameters of the category | The values |
---|---|
The height of the window (m) | 4 |
The width of the window (m) | 17.67 |
Thickness of concrete (m) | 0.2 |
Heat transfer coefficient of concrete | 0.11 |
Thickness of mortar (m) | 0.02 |
Heat transfer coefficient of mortar | 0.76 |
The thickness of insulating material (m) | 0.06 |
Heat transfer coefficient of insulating material | 0.08 |
The thickness of the window pane (m) | 0.012 |
Heat transfer coefficient of window | 5.7 |
Set indoor temperature in summer (°C) | 24 |
The reflection ratio of the floor | 0.5 |
The reflection ratio of the wall | 0.8 |
Window reflection ratio | 0.84 |
Ceiling reflection ratio | 0.9 |
The total transmittance of the window | 0.83 |
Density of concrete ( |
1947 |
Density of mortar ( |
1371 |
Density of insulating material ( |
162 |
Set indoor temperature in winter (°C) | 18 |
Finally, we give the PMV output results of different selection methods. As can be clearly seen from Figure
PMV output results of different selections.
To evaluate the effectiveness of NSGA-II in this article, we use the spacing (
Moment |
|
---|---|
8:00 | 0.014 |
10:00 | 0.067 |
12:00 | 0.655 |
14:00 | 0.454 |
16:00 | 0.665 |
18:00 | 0.133 |
9:00 | 0.147 |
11:00 | 0.011 |
13:00 | 0.075 |
15:00 | 0.085 |
17:00 | 0.624 |
From the values of
This paper introduces a design method of building energy conservation. To implement the model, the nondominant sorting genetic algorithm-II (NSGA-II) is written in the Python environment. In the design questions raised, the design parameters include window size, glass material and specification, floor veneer material, wall veneer material, external wall building material and specification, etc. In addition, the three objective functions of building thermal load, indoor thermal comfort, and lighting energy consumption are considered. Finally, 1100 solution sets are obtained at 11 different moments. In these solution sets, we choose two representative solution sets, which are, respectively, based on the minimum value of building cooling and heating load and the minimum value of lighting energy consumption.
NSGA-II has certain limitations. At present, in the research of energy-saving optimization, many scholars still use NSGA-II to solve three target problems and get excellent results [
The construction plan set out in the present paper does not mean specific cost analysis because cost analysis involves the impact of labour costs, construction technology, and local material price fluctuations. Identifiable cost issues can be further analysed on a case-by-case basis. Finally, the contradiction between building energy consumption and indoor environment is closely related to related climate characteristics and renewable energy. We will continue to explore the application and benefits of renewable energy in energy-efficient buildings in the next study.
Facade height with window
Heat consumption
Total annual lighting consumption
Cooling load
The glazing transmittance
Window area
The total room surface area
The area-weighted mean surface of the room
Temperature difference correction factor
The area of the envelope
Summation heat transfer coefficient
Heating interior design temperature
Outdoor design temperature for heating
Heat transfer coefficient of inner surface of envelope
Outdoor temperature fluctuation range in winter
The angular frequency
Orientation correction rate
The wind attachment rate
The external door attachment rate
The height addition rate of the building
The intermittent addition rate
Ventilation rate
Weight of the envelope
Density
Mean skin temperature
Operating temperature
Clothing thermal resistance
The air thermal resistance
Window height
Window width
Thickness of concrete
Thickness of mortar
The thickness of insulating material
The thickness of the window pane
The heat capacity of the air
Density of mortar
The density of insulating material
Set the indoor temperature in winter
Density of concrete
The area-weighted mean reflectance of the ceiling and walls above the midheight of the window excluding the window wall
Facade width with window
A coefficient depending on the obstruction angle
The area-weighted mean reflectance of the floor and walls below the midheight of the window excluding the window wall
The area-weighted mean reflectance of the ceiling and walls above the midheight of the window excluding the window wall
The length of the window pane
The vertical distance between the observation point and the window
The illuminance
The outdoor diffuse illuminance
The envelope basically consumes heat
The thickness of the material
Correction coefficient of material thermal conductivity
Thermal conductivity of each layer of envelope
Thermal resistance of closed space layer
Heat transfer coefficient of outer surface of envelope
Average calculated outdoor temperature in winter
The heat capacity of the envelope
Inner surface temperature of the enclosure
The convective coefficient between the external material and the external air
Area of external wall
Outdoor air temperature in summer
Convection coefficient between the envelope and the indoor air
The indoor heat source
Average outdoor air temperature in summer
Outdoor temperature fluctuation range in summer
Garment area coefficient
Thermal equilibrium coefficient
Metabolic rate
Mean radiation temperature
Heat transfer coefficient of concrete
Heat transfer coefficient of the whole window with closed space and window frame area accounting for 20%
Heat transfer coefficient of concrete
Heat transfer coefficient of insulating material
Set indoor temperature in summer
Floor reflection ratio
Wall reflection ratio
The reflection ratio of window glass
Ceiling reflection ratio
The total transmittance of the window
The area-weighted mean reflectance of the floor and walls below the midheight of the window excluding the window wall
A coefficient depending on the obstruction angle.
The data used to support the findings of this study are included within the article.
The authors declare no conflicts of interest.
This research is partially supported by the Youth Foundation for Humanities and Social Sciences of Ministry of Education of China (no. 19YJC630063) and the Youth Project of Education Department of Sichuan Province (no. 17ZB0335).