Intelligent Algorithm-Based Modeling of Lighting and Thermal Environment Balance in Green Buildings

1)

Static lighting evaluation standard

Static lighting evaluation criteria for the evaluation of the light environment are mainly based on the lighting coefficient and the indoor natural light level to evaluate two aspects. (1)

Lighting coefficient

Lighting coefficient is defined as a point in the indoor reference plane, by direct or indirect way, receiving from the assumption and known sky brightness distribution of the sky diffuse light and the illumination generated by the sky hemisphere at the same time in the outdoor unobstructed level of the sky diffuse illumination of the ratio.

The lighting coefficient C at a point indoors can be calculated by the following formula: (1)C=(En/Ew)*100%$$C = \left( {{E_n}/{E_w}} \right)*100\%$$

Where En is indoor illuminance and Ew is outdoor illuminance.

PMV (Predicted Mean Vote) represents the sensation of most people in the same environment, which can be used to evaluate the comfort level of a thermal environment, and is an evaluation index that characterizes the thermal response of the human body [22]. This index can be used to explore the relationship between human thermal sensation and human thermal load, and its experimental regression formula is as follows: (2)PMV=[0.303*e−0.036M+0.028]{M−W−3.05*10−3 [5733−6.99(M−W)−Pa]−0.42[(M−W)−58.15] −1.7*10−5M(5867−Pa)−0.0014M(34−ta) −3.96*10−8fcl[(tcl+273)4−(ts¯+2734]−fclhc(tcl−ta)}$$\begin{array}{l} PMV = \left[ {0.303*{e^{ - 0.036M}} + 0.028} \right]\left\{ {M - W - 3.05*{{10}^{ - 3}}} \right. \\ \left[ {5733 - 6.99(M - W) - {P_a}} \right] - 0.42[(M - W) - 58.15] \\ - 1.7*{10^{ - 5}}M\left( {5867 - {P_a}} \right) - 0.0014M\left( {34 - {t_a}} \right) \\ - 3.96*{10^{ - 8}}{f_{cl}}\left[ {{{\left( {{t_{cl}} + 273} \right)}^4} - } \right.\left. {\left. {\overline {\left( {{t_s}} \right.} + {{273}^4}} \right] - {f_{cl}}{h_c}\left( {{t_{cl}} - {t_a}} \right)} \right\} \\ \end{array}$$

Where: M indicates the rate of energy metabolism of the human body, which is determined by the amount of activity of the body, W/s. W indicates the amount of power done by the human body, W/s. P_a indicates the partial pressure of water vapor in the ambient air, Pa. t_a indicates the temperature of the air, ℃. f_cl indicates the ratio of the area of the clothed human body to the naked body. t_cl - Average temperature of the outer surface of the clothed human body, °C. h_c - Convective heat exchange coefficient, .

Based on the theoretical basis of the PMV indicator that when the human body is in a stable thermal environment, the greater the heat load on the human body, the further away from thermal comfort, the PMV indicator can be divided into 7 gradations: hot (+3), warm (+2), slightly warm (+1), moderate (0), slightly cool (-1), cool (-2), cold (-3).

On the basis of the PMV assessment index, the PPD of expectation unsatisfaction, which is the average number of votes of people’s expectations for hot and humid environments in hot and humid environments, was proposed with the formula: (3)PPD=100−95*exp(−0.03353*PMV4−0.2179*PMV2)$$PPD = 100 - 95*\exp \left( { - 0.03353*PM{V^4} - 0.2179*PM{V^2}} \right)$$

When more than 90% of the test population is satisfied with the thermal environment they are in, the PMV takes a value between -0.5 and 0.5. 1)

Indoor thermal comfort evaluation criteria

The indoor thermal comfort evaluation standard adopted in this paper evaluates the two categories of artificial cold and heat source heat and humidity environment and non-artificial cold and heat source heat and humidity environment respectively, and divides the indoor heat and humidity environment of the building into three levels, and proposes three evaluation methods, namely, graphical method, computational method and large-sample survey method respectively, and proposes the predicted adaptive mean thermal sensory index (APMV) as the evaluation basis in the non-artificial cold and heat source heat and humidity environment, and gives the calculation formula: (4)APMV=PMV/(1+λ*PMV)$$APMV = PMV/\left( {1 + \lambda *PMV} \right)$$

Where: λ denotes the adaptive coefficient and PMV denotes the expected average thermal sensation index.

In a non-artificial heat and humidity environment with cold and heat sources, class I (-0.5 ≤ APMV ≤ 0.5) indicates that 90% of the population is satisfied, class II (-1 ≤ APMV < -0.5 or 0.5 < APMV ≤ 1) indicates that 75% of the population is satisfied, and class III (APMV < -1 or APMV > 1) indicates that less than 75% of the population is satisfied. 2)

Indoor Thermal Comfort Interval

The IOS7730-2005 standard allows 10% of the population to be dissatisfied, and gives a recommended value of -0.5 to +0.5 for the PMV-PDD index, in which a specific range of comfort zones is given: the summer operating temperature is 24.5±1.5°C without air conditioning. The relative humidity is between 30% and 70%, and the average wind speed is <0.25 m/s. In winter, the operating temperature is 22±2.0°C, the relative humidity is between 30% and 70%, and the average wind speed is <0.15 m/s.

In this paper, the model is modeled according to three typical green buildings measured in the field, and the real building scale is extracted through the actual measurement of the research object and the analysis of the relevant drawings, because the selected green buildings are large-scale public buildings, and in order to improve the simulation efficiency, this paper extracts the relevant data of the green buildings, and models the building space, and in the post-simulation operation, the building shading and other components are modeled through Grasshopper. In the post-simulation calculation, the building components such as sunshade are modeled by Grasshopper.

According to the measured research on the light and heat environment of green buildings, green buildings mainly have the problems of uneven illumination, glare in some areas, high temperature in summer and low temperature in winter, and according to the statistics of the previous literature, the light and heat environment of green buildings is mainly affected by the lighting area, lighting orientation, external contact surface enclosure structure materials, building space temperature and other aspects.

Based on the limitations of green transformation of existing buildings, it is difficult to adjust the building orientation, change the building height-to-span ratio and other methods, and the consumption of manpower and material resources is large, which is not in line with the concept of energy saving. Therefore, in this paper, the main design variables extracted for green optimization of existing buildings include the type of building glass material, the scale of movable sunshades, and the lighting area.

In this study, only for the glass performance optimization research, so combined with the comprehensive literature and the norms given in the windows, doors, curtain wall solar heat gain coefficient formula, research on the glass heat gain coefficient and windows, doors, curtain walls in the transmittance part of the total transmittance ratio of the relationship between the fitted formula is: (5)SHGC=∑g$$SHGC = \sum g$$

∑g$$\sum g$$ indicates the total transmittance ratio of the light-transmitting part of the glass and doors, windows and curtain walls, and the total solar radiation transmittance ratio of the typical glass system.

In order to study the convenience, choose the size commonly used in the design as the value range of the sunshade, set the width of the sunshade and the interval value is the same, the value range of 0.1 ~ 1 m, change interval are 0.1 m. At the same time, set to the building of the window-wall area ratio of the value range of 0.1-0.9, the change interval is 0.1.

The PMV model overestimates the discomfort of the occupants in the warm conditions of a naturally ventilated building when it is built, which gives it some error when simulating in a building without air conditioning, so an adaptive model is chosen for the simulation, and the percentage of the total test duration that people are satisfied with their indoor comfort during the test time period is chosen as the optimization parameter for the indoor thermal environment, and the maximum value of this parameter is output in the Grasshopper to output percentOfTimeComfortable to pursue the maximum value of this parameter.

The total energy consumption of the building is the energy consumed by the building construction from the start of construction until it is put into operation at the end of construction. Narrowly speaking, the total building energy consumption refers to the energy consumption of the building that is put into operation and use after the construction of the building, including the building lighting energy consumption, heating energy consumption, cooling energy consumption, and equipment energy consumption. The building energy consumption selected in this thesis refers to the total building energy consumption in a narrow sense, excluding the energy consumption used in the construction phase of the building.

For the feasibility of later retrofit, the air conditioning energy consumption is increased in the simulation of the green building, so that a higher comfort level can be achieved under the natural and artificially regulated working conditions in the later retrofit process. Therefore, in this simulation, the sum of heating, cooling, and lighting energy is used to determine the total energy consumption of the building for later evaluation and analysis.

At the same time, in order to make an objective evaluation of the light environment inside the building, this paper selects the dynamic evaluation index to evaluate the light environment. Through the study of relevant norms and literature, the illuminance value within the range of 200-2000lux can meet the basic indoor illuminance while avoiding the hazard of glare, therefore, UDI_200-2000 is set as the indoor illuminance optimization index, and UDI_200-2000 is pursued as the maximum value.

1)

Multi-objective optimization

Multi-objective optimization is one of the branches of mathematical planning, which mainly studies the optimal solution of multiple objective functions in a certain region. In multi-objective optimization, due to the complexity of the objective function, there is a phenomenon of contradiction and incomparability between each other, a solution performs optimally on a specific objective, but may be poor or worst on other objectives.

Undominated solutions of multi-objective are defined as follows: assuming any two solutions S1, S2, for all the objectives (multi-objective), the case where S1 is better than S2 is known as S1 dominates S2, and at the same time, the solution of S1 is not dominated by any other solution, then S1 is the undominated solution of all the objectives, which is also known as the Pareto solution.

The set of these non-dominated solutions is known as the Pareto frontier. All solutions located in the Pareto front are not dominated by solutions outside the Pareto front, and thus these filtered “non-dominated solutions” have the least number of conflicting objectives.

The underlying model of multi-objective optimization can be expressed as follows: (6)Min/MaxO=(Ol,O2,…,On) Oi=fi(x) Constrainedby: C1(x)=m C2(x)>n$$\begin{array}{l} Min/MaxO = \left( {{O_l},{O_2}, \ldots ,{O_n}} \right) \\ Oi = fi(x) \\ Constrainedby: \\ {C_1}(x) = m \\ {C_2}(x) > n \\ \end{array}$$

Genetic algorithm is a class of heuristic algorithms, which is modeled on Darwin’s theory of biological evolution, and solves practical problems by simulating the natural selection process such as survival of the fittest and survival of the fittest in nature. The algorithm can automatically analyze and accumulate conditions about the search space in the process of searching for a solution, and in this way adaptively optimize the search process to obtain the best solution [23]. 1)

Genetic algorithm process

The basic flow of the traditional genetic algorithm is shown in Figure 2, with the following specific steps:

Step1: Determine the coding scheme of chromosomes, according to the pre-set population size, randomly generate the corresponding number of individuals to constitute the initial population, to achieve population initialization.

Step2: Calculate and record the fitness value of each individual within the population through the fitness function.

Step3: Judge whether the fitness value of the individuals in the population meets the target requirements or whether the iteration number reaches the maximum number of iterations, if it meets the conditions, then terminate the algorithm loop and output the optimal solution, otherwise, continue the execution.

Step4: Selection operator, screen out the excellent individuals with higher fitness within the population.

Step5: Crossover operator, pair individuals within the population two by two and exchange some genes with probability to generate new individuals.

Step6: Mutation operator, with a very small probability, make a gene of the individuals in the population mutate into a random gene.

Step7: Generate a new generation of population and execute Step2.

Figure 2.

Flowchart of genetic algorithm

Non-dominated Sorting Genetic Algorithm (NSGA) improves the genetic turnover operation on the basis of genetic algorithm by first stratifying for the dominant and non-dominant relationships of individuals in the population, and then carrying out the operations of replication, crossover, and mutation, which is able to reduce the amount of computation and save time. It also ensures the satisfaction of multi-objective optimization [24].

NSGA algorithm flow is shown in Fig. 3, and its advantages are reflected in the non-dominated ordering: the parent and child groups are merged, and different Pareto frontier groups are filtered sequentially according to the fitness of the objective function two-by-two comparison. In addition, to avoid the results converging only to individual solutions, different frontier populations have different fitness levels.

Figure 3.

Flowchart of the NSGA algorithm

NSGA-II is a widely used fast and excellent multi-objective algorithm, which is characterized by further fast ordering of non-dominated solution sets on top of NSGA.

In the basic NSGA operation, each solution within the population must be traversed and compared with the other solutions to obtain the dominance relation, which is arithmetic-heavy and computationally complex.NSGA-II, on the other hand, specifies the dominance relation by comparing the solutions in the population with a partially filled population. The P set is the initial population, and the solutions generated by cross mutation are continuously added to the set P′, and thereafter all other solutions obtained in the P population in the operation are compared with the solutions in the P set, and if p in P dominates any individual q in P′, q is eliminated at this point. Otherwise, if individual p is dominated by q in P′, then q is eliminated, and the cycle repeats to reach the iteration limit.

The comprehensive multi-objective optimization in this paper is performed based on Optimisation Optimization in DesignBuilder, which is a method of calculating different combinations of designs based on the NSGA-II genetic algorithm to achieve the goal of optimizing performance. Up to 10 combinations of design variables and 3 performance objectives are supported in DesignBuilder. 1)

Initial population

The initial population consists of variables that affect the objective function. The design variables in DB include form-related factors such as orientation, building orientation, structural factors such as window-to-wall ratios, window materials, exterior wall materials, system factors such as air conditioning design temperatures, heating and cooling types, solar PV panels, and equipment factors such as lighting type selection. Each variable can have several different options.

The final results of the bi-objective optimization of the southbound model are shown in Figure 4. The number of final optimization results is small, and according to the objective value convergence plot, it is found that the variation of the two objective values in the solution set is small, which indicates that the problem exists a superior solution that enhances both objectives simultaneously. The parameter value distance plot reflects the range to which the optimization parameters finally converge, i.e., large width, small angle and high height.

Figure 4.

Results of 30 generations of southbound dual-objective optimization

The three parameter optimization process is shown in Figure 5. The width is gradually shrinking to 2.2m, the angle value is in the range of 20-45°, and the height is also gradually shrinking to near 1.8m.

Figure 5.

Southbound parameter optimization process

The southward Pareto solution set is shown in Table 2. The southward Pareto front solution set has a full width of 2.2 m, angles between 23-42°, and heights of 1.7 m and 1.8 m. The overall energy saving effect is over 74%, and the indoor illumination can be increased by up to 3.48% of the time compared to the original case, improving the indoor lighting environment for 99 hours. Overall, the high energy saving effect causes a slight decrease in the indoor illumination situation, and the increase in the indoor illumination level reduces some of the energy saving gains, and there is a mutual trade-off in the optimal solution.

The final results of the bi-objective optimization of the eastward model are shown in Figure 6. The number of final optimization results is high, and the objective value convergence plot indicates that the solution set is more converged in terms of energy consumption and the solution set has a large range in terms of light. The parameter value distance plot reflects the final range that the optimization parameters converged to, i.e., large width, small to medium angle, and high height.

Figure 6.

Results of 30 generations of eastbound dual-objective optimization

The three parameter optimization search process is shown in Figure 7. The width shrinks to 2.2m when optimized to 400 times, the angle values range from 10-45°, and the height values are distributed between 0.4-1.8m.

Figure 7.

Eastbound parameter optimization process

The eastward Pareto solution set is shown in Table 3. The full width of the eastward Pareto front solution set is 2.2m, the angle is between 14-38°, and the height range is larger between 1.1m and 1.8m. The overall energy saving is between 45-52%, and the indoor light situation can be improved by 1.91% and also reduced by 6.24% compared to the initial situation. The mutual constraints in the optimal solution are greater than in the southward direction. The energy saving and light environment improvement effects are overall lower than the south-oriented case, which is mainly constrained by the building orientation, the available radiation in the east orientation is lower than that in the south orientation, and the original light environment is also worse than that in the south orientation. This comparison demonstrates the significance of building orientation.

The dry bulb temperatures at different heights in the experimental bench were monitored under eight shading and ventilation conditions, and a comparison of the temperature change curves at the lowest point (1 m above the ground) throughout the day is shown in Fig. 8. The horizontal coordinate indicates the time series from 0:00 to 24:00 throughout the day.

Figure 8.

Comparison of whole-day variation curves of dry bulb temperature

As can be seen from Figure 8, there is no obvious gradient in the air temperature at the lowest point of each condition, and the difference in temperature throughout the day is basically within 4°C, indicating that the change in conditions does not significantly improve the temperature in the area below a certain height.

The vertical temperature difference between the lowest point (1m) and the highest point (8m) of the room is further analyzed as shown in Fig. 9 and Fig. 10. Among them, Fig. 9’s (a) and (b) show the comparison of vertical temperature difference between different shading conditions under unventilated and ventilated conditions, respectively. Fig. 10’s (a) to (d) represent the comparison of vertical temperature difference for different ventilation conditions when the lighting area ratio is 80%, 60%, 40%, and 20%, respectively.

Figure 9.

Contrast of maximum vertical temperature difference in each sunshade condition

Figure 10.

Maximum vertical temperature difference in each centilation condition

On the one hand, the indoor vertical temperature differences of the four shading conditions are compared under the condition of fixed ventilation conditions. In the absence of roof exhaust, reduce the sunshade lighting area ratio, i.e., increase the roof shading area, reduce the shading coefficient, the indoor vertical temperature difference will be significantly reduced, the lighting ratio of every 20% reduction in the vertical temperature difference peak can be reduced by about 4 ℃, and the lower the shading coefficient, the more significant the effect of vertical temperature difference improvement. And in the ventilation conditions to reduce the light area ratio of the sunshade, 80% and 60% of the conditions of the vertical temperature difference is no significant difference, when the light ratio is reduced to 40% and 20%, the vertical temperature difference occurs a significant decline, at this time, the peak of the temperature difference throughout the day can be reduced to 4 ~ 8 ℃.

On the other hand, the vertical temperature difference between the two conditions of no ventilation and roof exhaust is compared under fixed shading conditions. The increase of roof ventilation in any shading condition can significantly reduce the vertical temperature difference, but the lower the shading coefficient, the smaller the reduction of vertical temperature difference before and after the increase of ventilation, i.e., the less significant the improvement effect of ventilation on the thermal environment. Among them, under the four shading conditions of 80%, 60%, 40% and 20%, the peak vertical temperature difference of the whole day can be reduced by 7.47℃, 5.22℃, 3.19℃ and 2.65℃ respectively after increasing ventilation.

Comprehensively comparing the vertical temperature difference under the above two quantitative conditions, it can be seen that only increasing the roof ventilation can make the maximum indoor vertical temperature difference drop significantly to below 6℃, while in order to achieve the same cooling effect under the condition of no ventilation, the shading panels need to be reduced to less than 20% of the lighting area ratio, which will greatly sacrifice the effect of indoor lighting at this time. Therefore, in order to improve the phenomenon of vertical temperature difference and at the same time take into account the indoor lighting, the use of roof shading + local exhaust ventilation is a more effective heat insulation measures. The combination of the above conditions is relatively better: 40% lighting area ratio + roof local exhaust. This program can reduce the peak indoor vertical temperature difference to 6℃ under the premise of fully ensuring indoor lighting.

In order to study the heat transfer characteristics of the roof envelope, the temperature difference between the inner and outer surfaces of the glass for each working condition is analyzed comparatively as shown in Figures 11 and 12. Among them, Fig. 11’s (a) and (b) show the temperature difference comparisons of different shading conditions without ventilation and with ventilation, respectively, and Fig. 12’s (a) to (d) indicate the temperature difference comparisons of different ventilation conditions when the lighting area ratio is 80%, 60%, 40% and 20%, respectively.

Figure 11.

Temperature difference between glass surfaces in each sunshade condition

Figure 12.

Temperature difference between glass surfaces in each ventilation condition

First of all, the temperature difference between the inner and outer surfaces of the glass changes throughout the day, starting from 6:00-8:00 a.m., the temperature difference between the inner and outer surfaces of the glass first undergoes a significant decline, and reaches a minimum in the 10:00-12:00 time period, with a temperature difference valley value of about -2~0℃, indicating that the inner surface temperature is lower than the outer surface of the phenomenon at this point in time. After that, the temperature difference starts to increase continuously and reaches the peak value in the afternoon 14:00-17:00, and the difference of the temperature difference peak value for different working conditions is 3~9℃. After the peak, the temperature difference decreases again and finally reaches relative stability, fluctuating within 3~5℃ at night. To summarize, the temperature difference between the inner and outer surfaces of the glass went through the process of decreasing - increasing - decreasing - stable fluctuation throughout the day.

Secondly, under fixed ventilation conditions, the temperature difference between the inner and outer surfaces of the glass is compared to the trends of the four shading conditions. When the lighting area ratio is increased, i.e., when the shading coefficient is increased, the temperature difference between the internal and external surfaces of the glass is significantly reduced during the daytime, and the peaks and valleys of the fluctuations throughout the day are delayed to a certain extent. The lower the shading coefficient, the longer the delay. The reduction of temperature difference values by shading is more significant when there is no ventilation.

Finally, the temperature difference between the inner and outer surfaces of the glass is compared between the no-ventilation and roof exhaust conditions under fixed shading conditions. As can be seen from Figure 12, under the shading condition of 80% lighting ratio, increasing roof ventilation can significantly reduce the temperature difference between the inner and outer surfaces of the glass, with the minimum value of the temperature difference decreasing from 2.47℃ to 0.27℃, and the maximum value of the temperature difference decreasing from 12.62℃ to 6.93℃. The effect of ventilation on the temperature difference of the glass is smaller under shading conditions with 60%, 40%, and 20% lighting ratio. It can be seen that when the daylighting ratio is reduced below a certain level, the improvement effect of ventilation on the temperature difference between the inner and outer surfaces of the glass is not significant. The values of temperature difference fluctuated within the range of -2~8°C throughout the day when roof exhaust ventilation was increased under the four working conditions.

In summary, the temperature of the inner and outer glass surfaces is affected by a combination of shading and ventilation. Among them, ventilation can reduce the value of temperature difference to a certain extent, but the reduction is small under the condition of low light ratio. Shading not only significantly reduces the temperature difference, but also delays the peak and trough moments of the temperature difference.

In addition, when the lighting area ratio is reduced to 40% and below, and the roof exhaust is increased at the same time, the temperature difference between the inside and outside of the glass appears to be negative at 8:00-14:00, i.e., the temperature of the inside surface of the glass is lower than that of the outside surface at that time, and the direction of the heat conduction and transfer is from the outdoors to the indoors.

When shading measures are used to reduce indoor temperature and improve the vertical temperature gradient, the level of indoor lighting changes significantly. To study the effect of four shading conditions on daylighting, a comparative analysis of the indoor illuminance under each condition is shown in Fig. 13, with (a) and (b) indicating the two different conditions, unventilated and ventilated, respectively.

Figure 13.

Contrast of indoor illuminance in each sunshade condition

When the roof lighting area ratio (or shading coefficient) is reduced, the indoor illuminance decreases linearly, and the peak indoor illuminance under 80%, 60%, 40%, and 20% working conditions is about 7000 lux, 4000 lux, 2500 lux, and 1500 lux, respectively. Among them, the peak indoor illuminance corresponding to an 80% lighting ratio is significantly higher than the other three conditions, and there is a significant fluctuation before and after noon.

Analyzing the reasons for the above fluctuation phenomenon, it can be seen that 80% is the case of no sunshade, at this time, the roof opaque component is only a steel frame, the indoor illuminance measurement point at a height of 1.5m is exposed to direct sunlight for a relatively long period of time before and after noon, so the test value appears to be a jagged fluctuation with a sudden increase and a sudden decrease. After the roof is covered with a sunshade, the indoor illuminance measurement points are mainly exposed to the sun’s scattered radiation, which significantly reduces and stabilizes the illuminance values.

Combined with the actual light roof green building on-site research and the above measured data, it can be seen that in the summer around noon when the intensity of solar radiation is high and the sun’s altitude angle is large, the light roof area of the green building is prone to a sudden increase in illuminance due to direct sunlight, resulting in the phenomenon of glare, and at this time, it is necessary to take certain shading measures to improve the indoor thermal and optical environments. Measured data show that to effectively reduce the drastic fluctuation of illuminance caused by direct sunlight indoors to improve the glare phenomenon, the roof lighting ratio can be reduced to 60% during the 10:00-14:00 time period, i.e., the shading coefficient is reduced to less than 0.42.