Energy-saving optimization method for roof greening system

Abstract

The invention relates to an energy-saving optimization method for a roof greening system. The method is suitable for energy-saving and environment-friendly technical fields. According to the technical scheme, the method comprises the steps of obtaining a matrix combination and a thickness range of at least one soil matrix, and randomly generating multiple matrix thicknesses; determining a leaf area index range of the roof greening system based on the location of the roof greening system to be optimized; constructing a building energy consumption model based on the climate information of the location of the roof greening system to be optimized and the basic condition of the building; based on the matrix combination and the matrix thickness of the soil matrix, determining thermophysical characteristics of the soil matrix, inputting the thermophysical characteristics and the leaf area index of the soil matrix into a building energy consumption model, and simulating to obtain annual thermal energy consumption, cold energy consumption and total energy consumption data of a building; determining the optimal matrix combination, matrix thickness and leaf area index of the roof greening system based on the heat energy consumption, cold energy consumption and total energy consumption data; and determining an optimal plant combination of the roof greening system based on the optimal leaf area index of the roof greening system.

Description

Technical Field

[0001] The invention relates to an energy-saving optimization method for a roof greening system, which is applicable to the technical field of energy-saving and environmental protection. Background Art

[0002] The acceleration of urbanization has led to a sharp increase in building energy consumption, especially in areas with hot summers and cold winters, where the heating and cooling needs of buildings have put tremendous pressure on energy consumption. Roof greening, as a green building technology, can significantly improve the energy performance of buildings, reduce the energy consumption of air conditioning and heating, and have a positive impact on the urban heat island effect.

[0003] In the existing technology, although roof greening is widely regarded as an effective energy-saving measure, there is a lack of systematic and targeted selection of plants and soil substrates. At present, there is no comprehensive and in-depth research and guidance on how the relevant factors of plants and soil substrates work together to optimize building energy consumption, and how to select plants and soil substrates that are most suitable for the climate characteristics of hot summers and cold winters. Summary of the invention

[0004] The technical problem to be solved by the present invention is: to provide an energy-saving optimization method for a roof greening system in view of the above-mentioned problems.

[0005] The technical solution adopted by the present invention is: a method for energy-saving optimization of a roof greening system, characterized by comprising:

[0006] obtaining a matrix combination of at least one soil matrix and a thickness range of the soil matrix, and randomly generating a plurality of matrix thicknesses from the thickness range;

[0007] Based on the location of the green roof system to be optimized, a leaf area index range of the green roof system is determined, and a plurality of leaf area indexes are randomly generated from the leaf area index range;

[0008] Based on the climate information of the location of the green roof system to be optimized and the basic conditions of the building corresponding to the green roof system, a building energy consumption model is constructed;

[0009] Based on the matrix combination and matrix thickness of the soil matrix, the thermophysical properties of the soil matrix are determined, and the thermophysical properties and leaf area index of the soil matrix are input into the building energy consumption model to simulate the building's annual heat energy consumption, cold energy consumption and total energy consumption data;

[0010] Based on the annual heat energy consumption, cooling energy consumption and total energy consumption data, the optimal substrate combination, substrate thickness and leaf area index of the roof greening system are determined;

[0011] Based on the optimal leaf area index of the roof greening system, the optimal plant combination of the roof greening system is determined.

[0012] The step of determining the leaf area index range of the roof greening system based on the location of the roof greening system to be optimized includes:

[0013] Based on the location of the green roof system, determine the plant species that are suitable for growing in the location of the green roof system;

[0014] Based on the planting density requirements, plant structure and leaf characteristics of the identified plant species, the leaf area index range of the roof greening system is determined.

[0015] The thermophysical properties of the soil matrix and the leaf area index are input into the building energy consumption model to simulate the building's annual heat energy consumption, cold energy consumption and total energy consumption data, including:

[0016] The simulation is performed using EnergyPlus software.

[0017] The optimal plant combination includes one or more of ground cover plants, herbaceous plants and shrub plants.

[0018] The ground cover plants include one or more of Sedum, Creeping Pot Grass, Petunia, Sedum sedum, Sedum sedum, Sedum sedum, Sedum sedum, Ophiopogon japonicus, Clover, Hosta, Macrophylla and Creeping Thyme;

[0019] The herbaceous plants include one or more of ryegrass, zoysia grass, iris, gentiana, lavender, tansy and tall fescue;

[0020] The shrub plants include one or more of boxwood, dwarf hydrangea, pomegranate, honeysuckle, jasmine and juniper.

[0021] The matrix combination of the soil matrix includes coconut husk, peat soil, perlite and vermiculite.

[0022] Also includes:

[0023] Obtain the planting depth requirements of each plant in the optimal plant combination, and determine whether the optimal substrate thickness meets the planting depth requirements of each plant;

[0024] If it is not satisfied, then based on the optimum substrate thickness, the thickness of the soil substrate in the planting area of ​​the corresponding plant is increased to meet the planting depth requirement of the plants in the area.

[0025] A roof greening system energy-saving optimization system, characterized by comprising:

[0026] A matrix information module, used to obtain a matrix combination of at least one soil matrix and a thickness range of the soil matrix, and randomly generate a plurality of matrix thicknesses from the thickness range;

[0027] A plant information module is used to determine a leaf area index range of the roof greening system based on the location of the roof greening system to be optimized, and randomly generate a plurality of leaf area indexes from the leaf area index range;

[0028] A model building module is used to build a building energy consumption model based on the climate information of the location of the green roof system to be optimized and the basic building conditions of the building corresponding to the green roof system;

[0029] The simulation module is used to determine the thermophysical properties of the soil matrix based on the matrix combination and matrix thickness of the soil matrix, input the thermophysical properties of the soil matrix and the leaf area index into the building energy consumption model, and simulate the heat energy consumption, cooling energy consumption and total energy consumption data of the building throughout the year;

[0030] A data analysis module is used to determine the optimal substrate combination, substrate thickness and leaf area index of the roof greening system based on the annual heat energy consumption, cooling energy consumption and total energy consumption data;

[0031] The plant determination module is used to determine the optimal plant combination of the roof greening system based on the optimal leaf area index of the roof greening system.

[0032] A storage medium stores a computer program that can be executed by a processor, characterized in that when the computer program is executed, the steps of the energy-saving optimization method of the roof greening system are implemented.

[0033] An electronic device has a memory and a processor, wherein the memory stores a computer program that can be executed by the processor, and is characterized in that when the computer program is executed, the steps of the energy-saving optimization method of the roof greening system are implemented.

[0034] The beneficial effect of the present invention is that the present invention generates multiple experimental groups based on substrate combination, substrate thickness and leaf area index, and inputs the generated experimental groups into the building energy consumption model to simulate the building's annual heat energy consumption, cold energy consumption and total energy consumption data, thereby determining the optimal substrate combination, substrate thickness and leaf area index of the roof greening system based on the heat energy consumption, cold energy consumption and total energy consumption data, and determining the plant combination based on the optimal leaf area index, thereby achieving energy-saving optimization of the roof greening system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of Example 1.

[0036] Figure 2 The energy consumption data of different LAI under 10cm substrate thickness according to the embodiment of the present application is compared with that of the bare roof. Figure 1 .

[0037] Figure 3The energy consumption data of different LAI under 10cm substrate thickness according to the embodiment of the present application is compared with that of the bare roof. Figure 2 .

[0038] Figure 4 The energy consumption data of different LAI under 20cm substrate thickness according to the embodiment of the present application is compared with that of the bare roof. Figure 1 .

[0039] Figure 5 The energy consumption data of different LAI under 20cm substrate thickness according to the embodiment of the present application is compared with that of the bare roof. Figure 2 .

[0040] Figure 6 The energy consumption data of different LAI under 30cm substrate thickness according to the embodiment of the present application is compared with that of the bare roof. Figure 1 .

[0041] Figure 7 The energy consumption data of different LAI under 30cm substrate thickness according to the embodiment of the present application is compared with that of the bare roof. Figure 2 .

[0042] Figure 8 The energy consumption data of different substrate thicknesses when LAI=0.5 according to the embodiment of the present application is Figure 1 .

[0043] Fig. 9 The energy consumption data of different substrate thicknesses when LAI=0.5 according to the embodiment of the present application is Figure 2 .

[0044] Fig.10 The energy consumption data of different substrate thicknesses when LAI=1 according to the embodiment of the present application is Figure 1 .

[0045] Fig.11 The energy consumption data of different substrate thicknesses when LAI=1 according to the embodiment of the present application is Figure 2 .

[0046] Fig.12 The energy consumption data of different substrate thicknesses when LAI=2 according to the embodiment of the present application Figure 1 .

[0047] Fig.13 The energy consumption data of different substrate thicknesses when LAI=2 according to the embodiment of the present application Figure 2 .

[0048] Fig.14 The energy consumption data of different substrate thicknesses when LAI=3 according to the embodiment of the present application is Figure 1 .

[0049] Fig.15 The energy consumption data of different substrate thicknesses when LAI=3 according to the embodiment of the present application is Figure 2 . DETAILED DESCRIPTION

[0050] Example 1: Figure 1 As shown, this embodiment is a method for optimizing energy conservation of a roof greening system, which specifically includes the following steps:

[0051] S1. Obtain at least one matrix combination of a soil matrix and a thickness range of the soil matrix, and randomly generate a plurality of matrix thicknesses from the thickness range.

[0052] When choosing a lightweight soil matrix suitable for plant growth, it is necessary to consider aspects such as soil drainage, aeration, water retention capacity and nutrient content. According to the heat consumption and characteristics of various soil matrices, several matrices can be selected to mix to form a lightweight matrix suitable for plant growth. The following Table 1 is a recommended matrix combination:

[0053] Table 1 Roof greening module matrix combination

[0054]

[0055] In this embodiment, the thickness range of the soil matrix is ​​determined based on the bearing capacity of the building roof, and a plurality of soil matrix thicknesses that meet the thickness range requirements are randomly generated. For ease of explanation, it is assumed that the matrix thicknesses of the randomly generated soil matrix in this example are 10 cm, 20 cm, and 30 cm.

[0056] S2. Based on the location of the roof greening system to be optimized, determine the leaf area index (LAI) range of the roof greening system, and randomly generate multiple leaf area indexes from the leaf area index range.

[0057] S2-1. Based on the location of the green roof system to be optimized, select plant species suitable for growing at the location of the green roof system to be optimized from a preset green roof plant library.

[0058] In this embodiment, the roof greening plant library includes ground cover plants, herbaceous plants and shrub plants, among which:

[0059] Ground cover plants include: Sedum, Creeping Grass, Petunia, Sedum sedum, Golden Leaf Sedum, White Flower Sedum, Japanese Sedum, Dwarf Ophiopogon, Clover, Hosta, Macrophylla, Creeping Thyme;

[0060] Herbs include: ryegrass, zoysia, iris, calendula, lavender, tansy, tall fescue;

[0061] Shrubs include: boxwood, dwarf hydrangea, pomegranate, honeysuckle, forsythia, and juniper.

[0062] S2-2. Based on the planting density requirements, plant structure and leaf characteristics of the plant species determined in step S2-1, and the preset combination of multiple plants, determine the leaf area index range of the roof greening system.

[0063] Through the reasonable combination of plants, roof greening can not only provide visual beauty, but also effectively reduce building energy consumption, regulate temperature and improve the environment through the leaf area, carbon fixation capacity and species characteristics of plants. The following table 2 shows some preset plant combinations:

[0064] Table 2 Plant combination forms of roof greening modules

[0065]

[0066]

[0067] S2-3. Randomly generate multiple leaf area indices that meet the leaf area index range requirement in step S2-2. For ease of explanation, assume that the randomly generated leaf area indices in this example are 0.5, 1, 2, and 3.

[0068] S3. Based on the climate information of the location of the green roof system to be optimized and the basic architectural conditions of the building corresponding to the green roof system, the EnergyPlus software is used for simulation to construct a building energy consumption model.

[0069] The location of the rooftop greening system to be optimized in this embodiment belongs to the subtropical monsoon climate, with an average annual temperature of 18.2℃ and an average annual precipitation of 1632.9mm. The air is humid but the precipitation is unevenly distributed throughout the year. The building corresponding to the rooftop greening system has four floors, the first floor is 5.4m high, and the other floors are 4.5m high. The total air-conditioned area is 7200m 2 The total building roof area is 1800m 2 The planned area of ​​the roof greening module is 1700m 2 .

[0070] The different structural layers of the building have different thicknesses, thermal conductivity, thermal storage coefficient, specific heat and density. The roof is composed of fine stone concrete, cement mortar isolation layer, cement mortar leveling layer, reinforced concrete roof panel and mixed mortar. The exterior wall includes cement mortar, sintered shale porous bricks and mixed mortar. The interior partition wall is composed of mixed mortar, autoclaved sand aerated concrete blocks (B06) and mixed mortar. The structure of the floor is fine stone concrete, cement mortar, reinforced concrete floor and mixed mortar. The ground is composed of cement mortar and fine stone concrete. The exterior windows are made of aluminum alloy hollow glass windows.

[0071] The thermophysical properties of each structural layer have a significant impact on the overall thermal performance of the building. Specific parameters such as thermal conductivity are shown in Table 3.

[0072] Table 3 Structure and materials of the building prototype

[0073]

[0074]

[0075] The simulation of this embodiment is based on the current public building energy-saving design specifications. The air conditioning operating temperature in the hot summer and cold winter areas is set to 26°C in summer and 18°C ​​in winter. In addition, the lighting energy consumption is set to 720.78GJ, the equipment energy consumption is 157.08GJ, and the fresh air volume is 11.24m 3 / s. Specific parameters are shown in Table 4:

[0076] Table 4 Main parameters of Energyplus energy consumption simulation

[0077]

[0078] S4. Based on the matrix combination and matrix thickness of the soil matrix, the thermophysical properties of the soil matrix are determined, and the thermophysical properties and leaf area index of the soil matrix are input into the building energy consumption model to simulate the building's annual heat energy consumption, cold energy consumption and total energy consumption data.

[0079] In this embodiment, 12 experimental groups (experimental groups A to L) were designed based on the soil matrix thickness of 10 cm, 20 cm, and 30 cm, and the leaf area index of 0.5, 1, 2, and 3, and the exposed roof was set as the control group. EnergyPlus software was set to perform simulation, and the simulation cycle was set to one year to ensure that all seasonal climate changes were included. The heat energy consumption, cooling energy consumption, and total energy consumption of the building were recorded, as shown in Table 5:

[0080] Table 5 Heating energy consumption, cooling energy consumption and total energy consumption of simulated experimental buildings

[0081]

[0082] As can be seen from Table 5, high LAI and thick substrate layers show significant potential for cooling in summer. However, although this combination slightly increases thermal energy consumption, overall, adding roof greening can significantly reduce the total annual energy consumption of the building.

[0083] Specifically, the combination of LAI = 3 and substrate thickness of 30 cm was the most effective in reducing total energy consumption, with a total annual energy consumption of 770.12 GJ, which was 94.45 GJ lower than the annual energy consumption of 864.57 GJ for the bare roof, indicating that the combination of high LAI and thick substrate layer has a higher efficiency in energy saving. Although the combination of high LAI and thick substrate layer slightly increased thermal energy consumption, the impact of this increase was relatively small, and the total energy consumption was still significantly reduced. The increase in thermal energy consumption after adding roof greening can be attributed to the heat capacity effect and transpiration effect. Plants and soil have a large heat capacity and can store more heat. During the day, these materials absorb and store solar energy, and release it at night, resulting in an increase in thermal energy consumption. In addition, high LAI plants have stronger transpiration, which significantly reduces the temperature during the day through transpiration. However, at night, due to the reduction of plant transpiration, the evaporative heat absorption effect is also reduced accordingly, resulting in a relative increase in thermal energy consumption.

[0084] S5. Based on the annual heat energy consumption, cooling energy consumption and total energy consumption data, determine the optimal substrate combination, substrate thickness and leaf area index of the roof greening system.

[0085] In order to verify the effect of changes in plant leaf area index (LAI) on roof greening energy consumption, the effect of different LAI values ​​on building energy consumption under the same substrate thickness was compared with the bare roof in the control group. By analyzing and statistically analyzing the differences in heat energy consumption, cooling energy consumption, and total energy consumption between different LAI values ​​and bare roofs under the same substrate thickness, the effect of LAI in reducing building energy consumption was quantified, such as Figure 2-7 shown.

[0086] It can be seen that from the change of thermal energy consumption, the increase of LAI under different substrate thicknesses has a relatively gentle effect on thermal energy consumption, with little fluctuation. This result shows that the contribution of the increase of LAI to the thermal insulation effect of the roof in winter is relatively limited. Although high LAI plants can significantly reduce the temperature during the day through shading effect and transpiration, the release of heat at night due to the reduction of plant transpiration leads to an increase in thermal energy consumption. Especially in winter, the shading effect of plants may lead to a decrease in solar radiation during the day, thereby reducing the passive heating effect of the building and increasing thermal energy consumption.

[0087] The change in cooling energy consumption is generally consistent with the trend of total energy consumption. Under different substrate thicknesses, cooling energy consumption decreased significantly when the LAI value increased from 0.5 to 3. When the substrate thickness was 10 cm, cooling energy consumption was most sensitive to LAI and the reduction in cooling energy consumption was also the largest. This result shows that high LAI plants effectively reduce roof temperature through transpiration and shading effects in summer, thereby reducing the cooling energy consumption of buildings. At LAI values ​​of 0.5 and 1, the reduction in cooling energy consumption was slightly greater than the thermal energy consumption, but when the LAI value increased to 2, the change in cooling energy consumption was lower than the thermal energy consumption for the first time, and this trend continued when the LAI value was 3.

[0088] Under the same substrate thickness, with the increase of LAI, the total energy consumption shows a trend of gradual decrease. For roof greening with substrate thickness of 10cm and 20cm, when LAI=2, the total energy consumption decreases significantly, and when LAI=3, the total energy consumption reaches the minimum. Although this trend is not as obvious as the previous two when the substrate thickness is 30cm, the total energy consumption still decreases with the increase of LAI. This shows that although the thick substrate layer has good thermal insulation performance to some extent, the increase of LAI can still further optimize its energy consumption performance.

[0089] When the substrate thickness is 10cm and 20cm, the increase in LAI value has a significant effect on reducing total energy consumption and cold energy consumption. When the substrate thickness is 30cm, although the total energy consumption still decreases with the increase in LAI value, the change is smaller. This may be because the thicker substrate layer itself already has good thermal insulation properties, and the increase in LAI has a relatively limited effect on its energy consumption optimization. However, the significant effect of high LAI plants in cooling has been verified, which is of great significance for reducing cooling energy consumption in summer. Compared with thermal energy consumption, cold energy consumption is more sensitive to LAI, which means that the transpiration and shading effect of plants have a more significant cooling effect on buildings in summer.

[0090] In order to verify the effect of substrate thickness changes on roof greening energy consumption, the effect of different substrate thicknesses with the same LAI value on building energy consumption was compared with the bare roof in the control group. By analyzing and statistically analyzing the differences in heat energy consumption, cooling energy consumption, and total energy consumption between the same LAI and different substrate thicknesses and the bare roof, the effect of LAI in reducing building energy consumption was quantified. Figure 8-15 shown.

[0091] It can be seen that by comparing the energy consumption performance of different LAI values ​​under the same matrix thickness, the analysis results show that with the increase of LAI, the total energy consumption decreases significantly. However, this decreasing trend is most obvious when LAI = 0.5. When LAI increases to 3, the decreasing trend of total energy consumption tends to be flat.

[0092] Analysis of thermal energy consumption changes: Under the same LAI value, the increase in substrate thickness has little effect on thermal energy consumption. This may be due to the relatively limited effect of the heat capacity effect of the substrate layer in winter. Therefore, the increase in substrate thickness mainly reduces cold energy consumption in summer by delaying heat transfer, but has little effect on thermal energy consumption in winter.

[0093] Analysis of cold energy consumption changes: Cold energy consumption decreases with increasing substrate thickness, which is particularly evident when LAI = 0.5, 1, and 2. When LAI = 3, cold energy consumption no longer decreases significantly, indicating that the cooling effect of the thickened substrate layer tends to be saturated under high LAI conditions. The changing trends of cold energy consumption under different combinations of LAI values ​​and substrate thickness also show significant differences. In the cases of LAI = 0.5, 1, and 2, cold energy consumption continues to decrease with increasing substrate thickness. This phenomenon indicates that the thickened substrate layer can effectively block solar radiation and reduce the heat transferred to the interior of the building, thereby reducing cold energy consumption. However, when LAI increases to 3, cold energy consumption no longer decreases significantly with increasing substrate thickness. This phenomenon may be due to the fact that high LAI plants have provided sufficient shading and transpiration. When the substrate thickness is further increased, the marginal effect on regulating the temperature inside the building is weakened, resulting in a flattening of the cold energy consumption reduction.

[0094] Analysis of total energy consumption changes: When the substrate thickness is fixed, the phenomenon that total energy consumption decreases with increasing LAI values ​​indicates that plants with high LAI values ​​effectively reduce the cooling energy consumption of buildings through their transpiration and shading effects, thereby reducing total energy consumption. For example, when the substrate thickness is 10 cm, the total energy consumption decreases significantly as the LAI increases from 0.5 to 3. This result is closely related to the ability of high LAI plants to release water and absorb heat through transpiration in summer. However, when the LAI reaches a certain level, that is, LAI = 3, the downward trend of total energy consumption becomes no longer significant. This may be because under high LAI conditions, the transpiration and shading effects of plants are close to saturation, and their contribution to further reducing energy consumption is limited.

[0095] In this embodiment, based on the energy consumption of each experimental group and the energy consumption of the control group, the energy consumption difference between the additional roof greening system and the bare roof is determined, the substrate cost of each experimental group is determined based on the substrate combination and substrate thickness in each experimental group, the corresponding plant combination is determined based on the LAI value in each experimental group, and the plant cost of each experimental group is determined based on the plant combination. Based on the energy consumption difference, substrate cost and plant cost, the cost per unit energy consumption difference is determined, and the optimal experimental group is determined, so that roof greening can achieve the best ecological and economic benefits.

[0096] S6. Based on the optimal leaf area index of the roof greening system, determine the optimal plant combination of the roof greening system from a plurality of preset plant combination forms.

[0097] S7, obtaining the planting depth requirement of each plant in the optimal plant combination determined in step S6, and judging whether the optimal substrate thickness meets the planting depth requirement of each plant. If not, based on the optimal substrate thickness, increasing the thickness of the soil substrate in the planting area of ​​the corresponding plant to meet the planting depth requirement of the plant in the area.

[0098] In this embodiment, when determining the thickness of the soil matrix, it is necessary to comprehensively consider the needs of different plants to ensure that each plant can grow in the appropriate soil depth. In order to achieve the best effect of roof greening, the thickness of the soil matrix can be increased in specific areas to meet the needs of deep-rooted plants while maintaining the lightweight design of the overall structure. This local thickening method can take into account the growth needs of different plants and optimize the ecological and economic benefits of roof greening, as shown in Table 6 below:

[0099] Table 6 Roof greening module matrix combination

[0100] Root characteristics Plant species Matrix thickness Shallow root plants Sedum, Creeping pot grass, Petunia, etc. 10-15 cm medium root plant Dwarf ophiopogon, clover, hosta, etc. 15-20 cm Deep rooted plants Boxwood, dwarf hydrangea, pomegranate, etc. 20-30 cm

[0101] Embodiment 2: This embodiment is an energy-saving optimization system for a roof greening system, including: a matrix information module, a plant information module, a model building module, a simulation module, a data analysis module and a plant determination module.

[0102] In this embodiment, the matrix information module is used to obtain a matrix combination of at least one soil matrix and a thickness range of the soil matrix, and randomly generate a plurality of matrix thicknesses from the thickness range.

[0103] In this example, the plant information module is used to determine the leaf area index range of the roof greening system based on the location of the roof greening system to be optimized, and randomly generate multiple leaf area indexes from the leaf area index range.

[0104] In this embodiment, the model building module is used to build a building energy consumption model based on the climate information of the location of the roof greening system to be optimized and the basic building conditions of the building corresponding to the roof greening system.

[0105] In this embodiment, the simulation module is used to determine the thermophysical properties of the soil matrix based on the matrix combination and matrix thickness of the soil matrix, input the thermophysical properties and leaf area index of the soil matrix into the building energy consumption model, and simulate the building's annual heat energy consumption, cooling energy consumption and total energy consumption data.

[0106] In this embodiment, the data analysis module is used to determine the optimal substrate combination, substrate thickness and leaf area index of the roof greening system based on the annual heat energy consumption, cooling energy consumption and total energy consumption data.

[0107] In this example, the plant determination module is used to determine the optimal plant combination of the roof greening system based on the optimal leaf area index of the roof greening system.

[0108] Embodiment 3: This embodiment is a storage medium on which a computer program that can be executed by a processor is stored. When the computer program is executed, the steps of the energy-saving optimization method for the roof greening system in Embodiment 1 are implemented.

[0109] Embodiment 4: This embodiment is an electronic device having a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, the steps of the roof greening system energy-saving optimization method in Embodiment 1 are implemented.

Claims

1. A method for energy saving optimization of a roof greening system, characterized in that: include: obtaining a matrix combination of at least one soil matrix and a thickness range of the soil matrix, and randomly generating a plurality of matrix thicknesses from the thickness range; Based on the location of the green roof system to be optimized, a leaf area index range of the green roof system is determined, and a plurality of leaf area indexes are randomly generated from the leaf area index range; Based on the climate information of the location of the green roof system to be optimized and the basic conditions of the building corresponding to the green roof system, a building energy consumption model is constructed; Based on the matrix combination and matrix thickness of the soil matrix, the thermophysical properties of the soil matrix are determined, and the thermophysical properties and leaf area index of the soil matrix are input into the building energy consumption model to simulate the building's annual heat energy consumption, cold energy consumption and total energy consumption data; Based on the annual heat energy consumption, cooling energy consumption and total energy consumption data, the optimal substrate combination, substrate thickness and leaf area index of the roof greening system are determined; Based on the optimal leaf area index of the roof greening system, the optimal plant combination of the roof greening system is determined.

2. The energy-saving optimization method for a roof greening system according to claim 1, characterized in that: The step of determining the leaf area index range of the roof greening system based on the location of the roof greening system to be optimized includes: Based on the location of the green roof system, determine the plant species that are suitable for growing in the location of the green roof system; Based on the planting density requirements, plant structure and leaf characteristics of the identified plant species, the leaf area index range of the roof greening system is determined.

3. The energy-saving optimization method for a roof greening system according to claim 1, characterized in that: The thermophysical properties of the soil matrix and the leaf area index are input into the building energy consumption model to simulate the building's annual heat energy consumption, cold energy consumption and total energy consumption data, including: The simulation is performed using EnergyPlus software.

4. The energy-saving optimization method for a roof greening system according to claim 1, characterized in that: The optimal plant combination includes one or more of ground cover plants, herbaceous plants and shrub plants.

5. The energy-saving optimization method for a roof greening system according to claim 4 is characterized in that: The ground cover plants include one or more of Sedum, Creeping Pot Grass, Petunia, Sedum sedum, Sedum sedum, Sedum sedum, Sedum sedum, Ophiopogon japonicus, Clover, Hosta, Macrophylla and Creeping Thyme; The herbaceous plants include one or more of ryegrass, zoysia grass, iris, gentiana, lavender, tansy and tall fescue; The shrub plants include one or more of boxwood, dwarf hydrangea, pomegranate, honeysuckle, jasmine and juniper.

6. The energy-saving optimization method for a roof greening system according to claim 1, characterized in that: The matrix combination of the soil matrix includes coconut husk, peat soil, perlite and vermiculite.

7. The energy-saving optimization method for a roof greening system according to claim 1, characterized in that: Also includes: Obtain the planting depth requirements of each plant in the optimal plant combination, and determine whether the optimal substrate thickness meets the planting depth requirements of each plant; If it is not satisfied, then based on the optimum substrate thickness, the thickness of the soil substrate in the planting area of ​​the corresponding plant is increased to meet the planting depth requirement of the plants in the area.

8. A roof greening system energy-saving optimization system, characterized in that: include: A matrix information module, used to obtain a matrix combination of at least one soil matrix and a thickness range of the soil matrix, and randomly generate a plurality of matrix thicknesses from the thickness range; A plant information module is used to determine a leaf area index range of the roof greening system based on the location of the roof greening system to be optimized, and randomly generate a plurality of leaf area indexes from the leaf area index range; A model building module is used to build a building energy consumption model based on the climate information of the location of the green roof system to be optimized and the basic building conditions of the building corresponding to the green roof system; The simulation module is used to determine the thermophysical properties of the soil matrix based on the matrix combination and matrix thickness of the soil matrix, input the thermophysical properties of the soil matrix and the leaf area index into the building energy consumption model, and simulate the heat energy consumption, cooling energy consumption and total energy consumption data of the building throughout the year; A data analysis module is used to determine the optimal substrate combination, substrate thickness and leaf area index of the roof greening system based on the annual heat energy consumption, cooling energy consumption and total energy consumption data; The plant determination module is used to determine the optimal plant combination of the roof greening system based on the optimal leaf area index of the roof greening system.

9. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the method for energy saving optimization of a roof greening system according to any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, wherein: When the computer program is executed, the steps of the method for energy saving optimization of a roof greening system according to any one of claims 1 to 7 are implemented.

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