Energy consumption optimization method for old house transformation

Abstract

The invention discloses an energy consumption optimization method for old house transformation. The method comprises the following steps: S1, scanning to obtain an infrared image of a thermal insulation layer of an old house; s2, importing the black, white, light and dark image of the thermal insulation layer into a parameterization platform; s3, generating an old house thermal insulation layer effective model through BIM software; s4, each enclosure face of the heat preservation layer model is equally sectioned, the heat transfer coefficient K of each section is calculated, and then the average heat transfer coefficient of each enclosure face is calculated according to the heat transfer coefficient K of the section; s5, a non-thickness planar model is established in the BIM, and then the enclosure structure of the old house is constructed according to the average heat transfer coefficient of the enclosure face and door and window materials; and S6, calculating the most economic thickness dopt of the thermal insulation material. Compared with the prior art, the method has the advantages that the construction heat flow of the enclosure structure can be accurately reflected for the old residence, and the real effect of the thermal insulation layer after long-term use can be accurately calculated; and a formula of the most economic thickness of the thermal insulation layer is deduced, and energy saving is achieved to the maximum.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy consumption optimization, and in particular to an energy consumption optimization method for old house reconstruction. Background Art

[0002] With the advancement of society, people's awareness of energy conservation and environmental protection continues to grow. As major energy consumers, it is imperative that buildings take measures to conserve energy and reduce emissions. Heat loss from the exterior envelope dominates the composition of building energy consumption, with heat transfer from the exterior walls accounting for approximately 30% of the building's total heat transfer energy consumption. This data highlights the critical role of strengthening exterior wall insulation in building energy conservation. Secondly, with the continuous improvement of economic levels, people's demand for indoor environmental comfort is increasing, which has directly led to a rapid increase in building energy consumption. Currently, most buildings in my country still use air conditioning systems as the primary means of cooling in summer and heating in winter. However, due to the insufficient thermal insulation performance of the building envelope, energy utilization efficiency is generally low. Summary of the Invention

[0003] (1) Technical issues to be solved

[0004] Based on this, the present invention proposes an energy consumption optimization method for old house renovation to solve the problem of how to optimize energy consumption and improve energy-saving effects in old houses.

[0005] (2) Technical solution

[0006] In order to overcome the above problems or at least partially solve the above problems, the present invention provides an energy consumption optimization method for old house renovation, comprising the following steps:

[0007] S1: Scan the exterior walls and roof of the old house to obtain the infrared image of the insulation layer of the old house;

[0008] S2: Convert the infrared image of the insulation layer into a black and white image, and then import it into the parametric platform of the BIM software;

[0009] S3: BIM software generates insulation layer models through black and white light and dark images;

[0010] S4: Equally cut each enclosure surface of the insulation layer model, generate multiple sections for each enclosure surface of the insulation layer model, calculate the heat transfer coefficient K of each section by formula (1) and formula (2), and then calculate the average heat transfer coefficient of each enclosure surface based on the heat transfer coefficient K of the sections of each enclosure surface of the insulation layer model;

[0011] R=1 / u (1)

[0012] K=1 / (R+Ri+Re) (2)

[0013] Where R is the thermal resistance of the cross section, u is the heat transfer coefficient of the cross section, Ri is the heat transfer resistance of the inner surface of the cross section, and Re is the heat transfer resistance of the outer surface of the cross section;

[0014] S5: Create a non-thick surface model in BIM, and then construct the enclosure structure of the old house based on the average heat transfer coefficient of each enclosure surface of the insulation layer model and the material of doors and windows;

[0015] S6: Calculate the most economical thickness d of the insulation material using formula (3) opt :

[0016]

[0017] Where Pe is the energy price (yuan / kWh), ΔT is the average temperature difference between indoor and outdoor (°C), H is the number of heating / cooling hours (h / year), λ is the thermal conductivity of the insulation material (W / m·K), and P m is the unit price of insulation materials, R0 is the other thermal resistance (m 2 K / W);

[0018] In the enclosure structure, formula (3) is programmed to establish a calculation module in the parametric platform, and the data interface is established for each value required by formula (3). The data required by the calculation module are input to obtain the economic insulation thickness d of the insulation material. opt Input construction, then according to d opt Input structure to replace the insulation layer of old houses;

[0019] S7: Install sunshades where there are windows in the old house and use a linkage control algorithm to save energy. The linkage control algorithm includes a first rule for reducing heat load by adjusting curtains, a second rule for adjusting air-conditioning temperature by PMV value, and a third rule for adjusting air-conditioning power by electricity price. The wallacei operator sets the angle and width of the sunshade as variables, and uses the comfort, energy consumption, illumination, and photovoltaic potential output by PMVPolygon, ModelToOSM, AnnualDaylight, and IncidentRadiatio as phenotypes. Through multi-objective optimization, multiple sets of optimal solutions are obtained when calculating the optimal energy saving value. Then, the width and angle of the sunshade are designed based on the multiple sets of optimal solutions, as well as the execution order of the three rules in each time period.

[0020] Preferably, in step S1, the outer walls and roof of the old house are scanned by an infrared scanner to identify the effectiveness of the insulation layer and generate an infrared image of the insulation layer.

[0021] Preferably, in step S2, the operation table of the electrical appliances in the room and the temperature and humidity settings of the air conditioner are imported into the ModelToOSM operator; at the same time, the epw meteorological file of the location of the old house is imported into the ModelToOSM operator, and the energy consumption status of the old house is simulated by the ModelToOSM operator, and the hourly energy consumption per square meter of the old house throughout the year is calculated.

[0022] Preferably, in step S3, the specific steps for the BIM software to generate the insulation layer model through the black and white light and dark image are: performing image data conversion on the imported black and white light and dark image through the image sampler operator of the BIM software to obtain a point set, and then generating a grid from the point set through the Conmesh operator, and adjusting the grid subdivision to form a smooth grid through the plug-in weavbird, thereby forming the insulation layer model.

[0023] Preferably, in step S6: Formula (3) is obtained by the following steps:

[0024] The full life cycle investment cost C is calculated by formula (31) total :

[0025] C total =C i +N·C e (31)

[0026] Among them, C i is the initial investment cost, N is the expected payback period, C e is the annual energy consumption price;

[0027] Calculate C using formula (32) e :

[0028] C e =Q·P e (32)

[0029] Among them, Q is the annual cooling and heating load, P e is the energy price (yuan / kWh);

[0030] Q is calculated using formula (33):

[0031]

[0032] Where ΔT is the average temperature difference between indoor and outdoor (°C), H is the number of hours of heating / cooling (h / year), d is the thickness of the insulation layer (m), λ is the thermal conductivity of the insulation material (W / m·K), and R0 is the thermal resistance other than the insulation layer (m 2 K / W);

[0033] C is calculated by formula (34) i :

[0034] C i =d·P m (34)

[0035] Among them, P m is the unit price of insulation materials;

[0036] Combining formulas (31), (32), (33) and (34) yields formula (35):

[0037]

[0038] According to the economic curve of the thermal insulation layer, it can be seen that for C in formula (35), total Find the derivative, and when the derivative is 0, the most economical thickness is obtained Preferably, in step S7, the sun visor angle is set to 0-45 degrees, and the sun visor width is set to 0.5-1 meter.

[0039] Preferably, in step S7, the comfort level is calculated by PMVPolygon through the enclosure structure, the epw weather file of the location of the old house, the linkage control algorithm, and the sunshade data;

[0040] The energy consumption value is calculated by ModelToOSM using the enclosing structure, the epw weather file of the old house location, three rules, and the air conditioning operation table data;

[0041] The illumination value is calculated by Annual Daylight using the enclosure structure, three rules, and the epw meteorological file data at the location of the old house;

[0042] The photovoltaic potential is calculated by IncidentRadiatio using three rules and the epw meteorological file data of the location of the old house.

[0043] Preferably, in step S7, the first rule includes the following steps:

[0044] When the light value is greater than 500 lux, close 80% of the curtains;

[0045] When the light value is 250-500 lux, close 50% of the curtains;

[0046] When the light value is less than 250lux, close 20% of the curtains;

[0047] The second rule includes the following steps:

[0048] When the PMV is greater than 0.5, it is too hot and the air conditioner temperature is lowered by 1 degree;

[0049] When the PMV is less than -0.5, it is too cold and the air conditioner temperature is increased by 1 degree;

[0050] The third rule includes the following steps:

[0051] When electricity prices are at their peak, the operating power of the air conditioner is reduced by 30%.

[0052] Preferably, in step S7, through the multi-objective optimization of the wallacei operator, the optimal phenotype parameter illumination value and photovoltaic potential can be weighed; when photovoltaic panels are used, the output data of the operation is Q2=Q1-n·Qg, where Q1 is the output data of ModelToOSM, n is the photovoltaic efficiency, and Qg is the thermal radiation received by the sunshade, and its value is calculated by IncidentRadiatio.

[0053] Preferably, in step S7, the sunshade is set above the south window of the old house, the sunshade is set above the east window of the old house and on the side close to the south, and the sunshade is set above the west window of the old house and on the side close to the south.

[0054] (3) Beneficial effects

[0055] The energy consumption optimization method for old house renovation of the present invention has the following advantages: The present invention achieves the following beneficial effects:

[0056] 1. For old houses, it can accurately reflect the structural heat flow of the enclosure structure and accurately calculate the actual effect of the insulation layer after long-term use.

[0057] 2. Modify the algorithm of Therm software (American standard) and increase the heat transfer coefficient of internal and external surfaces to make it more in line with domestic standards.

[0058] 3. Derive the formula for the most economical thickness of the insulation layer to maximize energy savings.

[0059] 4. The width and angle of the sun visor are designed through multi-objective optimization to further achieve energy saving.

[0060] 5. Through multi-objective optimization, the execution order of the three rules in each time period is designed to further improve the energy-saving effect of old houses and facilitate the implementation of smart homes. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the present invention in any way. In the accompanying drawings:

[0062] Figure 1 This is a schematic diagram of energy consumption assessment of old buildings according to the present invention;

[0063] Figure 2This is a schematic diagram of energy consumption optimization for old buildings according to the present invention;

[0064] Figure 3 It is a structural schematic diagram of the old house reconstruction of the present invention.

[0065] Description of reference numerals:

[0066] 1. Old house, 2. Sunshade, 3. Window. DETAILED DESCRIPTION

[0067] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0068] This embodiment provides an energy consumption optimization method for old house renovation. Figure 1 , including the following steps:

[0069] S1: Scan the exterior walls and roof of old house 1 to obtain an infrared image of the insulation layer of old house 1;

[0070] Specifically, an infrared scanner is mounted on a drone to scan the exterior walls, roof, and floor slabs of the old house 1, identify the effectiveness of the insulation layer, and generate an infrared image of the insulation layer. That is, the infrared scanner can generate a thermal image based on the heat transfer performance of the wall, which serves as the basis for subsequent image parameter generation and construction.

[0071] S2: Convert the infrared image of the insulation layer into a black and white light and dark image, and then import it into the parametric platform of the BIM software; specifically, convert the infrared image of the insulation layer into a black and white light and dark image using Photoshop software.

[0072] S3: BIM software generates insulation layer models through black and white light and dark images;

[0073] S4: Use BIM software to equally section each enclosure surface of the insulation layer model. Specifically, the enclosure surface is each wall, roof, and floor of the insulation layer model. Generate multiple sections for each enclosure surface of the insulation layer model, calculate the heat transfer coefficient K of each section using formula (1) and formula (2), and then calculate the average heat transfer coefficient of each enclosure surface based on the heat transfer coefficient K of each enclosure surface of the insulation layer model.

[0074] R=1 / u (1)

[0075] K=1 / (R+Ri+Re) (2)

[0076] Where R is the thermal resistance of the cross section, u is the heat transfer coefficient of the cross section, Ri is the heat transfer resistance of the inner surface of the cross section, and Re is the heat transfer resistance of the outer surface of the cross section;

[0077] Specifically, the average heat transfer coefficient of each enclosure surface of the insulation layer model is the sum of the heat transfer coefficients K of the cross sections of the surface divided by the number of cross sections of the surface.

[0078] Since therm software only calculates the heat transfer coefficient of the structure and does not calculate the heat transfer coefficient of the internal and external surfaces of the structure, formula (2) is introduced for calculation to obtain the true heat transfer coefficient of each wall, roof, and floor of the insulation layer model. Here, u is the heat transfer coefficient simulated and calculated by therm software, and Ri and Re can be queried based on the material.

[0079] Through the design of steps S1-S4, the structural heat flow of the envelope structure can be accurately reflected for old houses.

[0080] S5: Create a surface model without thickness in BIM, using the average heat transfer coefficient of each enclosure surface of the insulation layer model and the material of the doors and windows as the structure. Set the operating schedule for the room's electrical appliances according to the typical household start and stop times. Set the air conditioner's required temperature and humidity for cooling and heating based on the outside temperature, and import these into the ModelToOSM calculator. Simultaneously, import the epw weather file for Old House 1's location into the ModelToOSM calculator. The ModelToOSM calculator uses the energyplus energy calculation core, which has a pre-programmed algorithm. The ModelToOSM calculator accurately simulates Old House 1's energy consumption using the actual heat transfer coefficient of each enclosure surface of the insulation layer model, and calculates Old House 1's hourly energy consumption per square meter throughout the year. After optimizing Old House 1's energy consumption, compare the energy consumption before and after optimization, as well as the hourly energy consumption throughout the year, to determine the energy savings.

[0081] Refer to the attached Figure 2 , S6: Calculate the most economical thickness d of the insulation material using formula (3) opt :

[0082]

[0083] Where Pe is the energy price (yuan / kWh), ΔT is the average temperature difference between indoor and outdoor (°C), H is the number of heating / cooling hours (h / year), λ is the thermal conductivity of the insulation material (W / m·K), and P m is the unit price of insulation materials, R0 is the other thermal resistance (m 2 K / W);

[0084] In the enclosure structure, formula (3) is programmed to establish a calculation module in the parametric platform, and the data interface is established for each value required by formula (3). The data required by the calculation module are input to obtain the economic insulation thickness d of the insulation material. opt Input construction. Based on this d opt Input structure to replace the insulation layer of old house 1 and replace the door and window structure with energy-saving doors and windows to optimize the energy consumption of old house 1.

[0085] S7: Sunshades 2 are installed in windowed areas of old house 1, and a linkage control algorithm is used to achieve energy savings. The linkage control algorithm includes a first rule for reducing heat load by adjusting curtains, a second rule for adjusting air conditioning temperature by using PMV values, and a third rule for adjusting air conditioning power by using electricity prices. The wallacei operator (which includes a genetic algorithm) sets the angle and width of sunshade 2 as variables, ranging from 0 to 45°, and the width of sunshade 2 from 0.5 to 1 meter. The comfort level, energy consumption, illuminance, and photovoltaic potential output by PMV Polygon, ModelToOSM, Annual Daylight, and IncidentRadiatio, respectively, are used as phenotypes. Through multi-objective optimization, multiple optimal solution sets are obtained when calculating the optimal energy savings. These optimal solution sets are then used to design the width and angle of sunshade 2, as well as the execution order of the three rules for each time period. Specifically, a set of optimal solution sets is manually selected, and the parameters within them are used to design the width and angle of sunshade 2, as well as the execution order of the three rules for each time period.

[0086] Among them, in step S7, the comfort level is calculated by PMV Polygon through the enclosure structure, the epw weather file of the location of the old house 1, the linkage control algorithm, and the sunshade 2 data.

[0087] The energy consumption value is calculated by ModelToOSM through the envelope structure, the epw meteorological file of the location of old house 1, the linkage control algorithm, and the air conditioning operation table data.

[0088] In step S7, the illumination value is calculated by Annual Daylight through the enclosure structure, linkage control algorithm, and epw meteorological file data at the location of the old house 1.

[0089] In step S7 , the photovoltaic potential is calculated by IncidentRadiatio through the linkage control algorithm and the epw meteorological file data at the location of the old house 1 .

[0090] In step S7, refer to the attached Figure 3, the sunshade 2 is set above the south window 3 of the old house 1, the sunshade 2 is set above the east window 3 of the old house 1 and the side close to the south, and the sunshade 2 is set above the west window 3 of the old house 1 and the side close to the south;

[0091] In step S7, the first rule includes the following steps:

[0092] When the light value is greater than 500 lux, close 80% of the curtains, that is, the curtains cover 80% of the windows 3;

[0093] When the light value is 250-500 lux, close 50% of the curtains, that is, the curtains cover 50% of the windows 3;

[0094] When the light value is less than 250 lux, 20% of the curtains are closed, that is, the curtains cover 20% of the windows 3.

[0095] The second rule includes the following steps:

[0096] When the PMV is greater than 0.5, it is too hot and the air conditioner temperature is lowered by 1 degree;

[0097] When the PMV is less than -0.5, it is too cold and the air conditioner temperature is increased by 1 degree.

[0098] The indoor comfort can be improved by adjusting the second rule.

[0099] The third rule includes the following steps:

[0100] When electricity prices are at their peak, the operating power of the air conditioner is reduced by 30%.

[0101] Energy costs can be reduced through third-rule regulation.

[0102] Some of the code is as follows:

[0103] #Input: current PMV value, outdoor light intensity, electricity price period

[0104] defcontrol_loop(pmv,light_intensity,is_peak_hour):

[0105] #Rule 1: Prioritize adjusting curtains to reduce heat load

[0106] iflight_intensity>500:#Strong light

[0107] set_shade_coefficient(0.2)#Close 80% of the curtains

[0108] elif250 <light_intensit≤500:

[0109] set shade_coefficient(0.5)#Close 50% of the curtains

[0110] else:

[0111] set_shade_coefficient(0.8)#keep 20% shading

[0112] #Rule 2: Start the air conditioner when the PMV exceeds the comfort zone

[0113] ifpmv>0.5:#slightly hot

[0114] set_ac_temperature(current_temp-1)#Lower the temperature by 1℃

[0115] elifpmv<-0.5:#slightly cold

[0116] set_ac_temperature(current_temp+1)#Raise temperature by 1℃

[0117] #Rule 3: Limit air conditioning power during peak electricity price periods

[0118] ifis_peak_hour:

[0119] set_ac_max_power(70%)#Air conditioner runs at a maximum power of 70%

[0120] The illumination value in the first rule can be obtained from the epw meteorological file of the city where the old house 1 is located.

[0121] In step S3, the BIM software generates the insulation layer model from the black and white light and dark image. The BIM software's image sampler converts the imported black and white light and dark image into image data. This component generates data based on the image's color depth, then assigns coordinates to this data to form a point set. The point set is then meshed using the Conmesh component. The Weavbird plug-in adjusts the mesh subdivision to create a smooth mesh, ultimately forming the insulation layer model. This process ensures a realistic and effective insulation layer model.

[0122] In step S4, formula (1) and formula (2) are introduced into the programming language and connected to therm software to realize direct output of the heat transfer coefficient K value data. Formula (1) and formula (2) are compiled into a module and connected to therm software to make up for the shortcomings of therm software.

[0123] In step S6: Formula (3) is obtained by the following steps:

[0124] The full life cycle investment cost C is calculated by formula (31) total :

[0125] C total =C i +N·C e (31)

[0126] Among them, C i is the initial investment cost, N is the expected payback period, C e is the annual energy consumption price;

[0127] Calculate C using formula (32) e :

[0128] C e =Q·P e (32)

[0129] Among them, Q is the annual cooling and heating load, P e is the energy price (yuan / kWh);

[0130] Q is calculated using formula (33):

[0131]

[0132] Where ΔT is the average temperature difference between indoor and outdoor (°C), H is the number of hours of heating / cooling (h / year), d is the thickness of the insulation layer (m), λ is the thermal conductivity of the insulation material (W / m·K), and R0 is the thermal resistance other than the insulation layer (m 2 K / W);

[0133] C is calculated by formula (34) i :

[0134] C i =d·P m (34)

[0135] Among them, P m is the unit price of insulation materials;

[0136] Combining formulas (31), (32), (33) and (34) yields formula (35):

[0137]

[0138] According to the economic curve of the thermal insulation layer, it can be seen that for C in formula (35), total Find the derivative, and when the derivative is 0, the most economical thickness is obtained

[0139] As another embodiment of the present invention: in step S7, through the multi-objective optimization of the wallacei operator, the better phenotype parameters (illuminance value and photovoltaic potential) can be weighed; when photovoltaic panels are used, the output data of the operation is Q2 = Q1-n·Qg, where Q1 is the output data of ModelToOSM, n is the photovoltaic efficiency, and Qg is the amount of thermal radiation received by the sunshade 2, and its value is calculated by IncidentRadiatio.

[0140] The present invention has achieved the following beneficial effects:

[0141] 1. For old houses, it can accurately reflect the structural heat flow of the enclosure structure and accurately calculate the actual effect of the insulation layer after long-term use.

[0142] 2. Modify the algorithm of Therm software (American standard) and increase the heat transfer coefficient of internal and external surfaces to make it more in line with domestic standards.

[0143] 3. Derive the formula for the most economical thickness of the insulation layer to maximize energy savings, and integrate it into the parametric platform so that it can be obtained by simply inputting parameters, thereby improving efficiency.

[0144] 4. Taking full consideration of the sunlight exposure characteristics of south shading and east-west shading, horizontal shading is used on the south side and L-shaped shading is used on the east-west side. The multi-objective algorithm is integrated to optimize the width and angle of the sunshade 2 for the best energy-saving effect, which also has good lighting and photovoltaic potential.

[0145] 5. Use parameters such as sunshine, PMV, and peak electricity prices to optimize air conditioning and curtain operating parameters, and design the execution order of three rules for each time period to further improve the energy saving effect of the old house 1, while also facilitating the implementation of smart home.

[0146] Finally, the method of this application is only a preferred embodiment and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0147] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for optimizing energy consumption in old house renovation, characterized in that: The steps include: S1: Scan the exterior walls and roof of the old house to obtain the infrared image of the insulation layer of the old house; S2: Convert the infrared image of the insulation layer into a black and white light and dark image, and then import it into the parameterization platform; S3: BIM software generates insulation layer models through black and white light and dark images; S4: Equally cut each enclosure surface of the insulation layer model, generate multiple sections for each enclosure surface of the insulation layer model, calculate the heat transfer coefficient K of each section by formula (1) and formula (2), and then calculate the average heat transfer coefficient of each enclosure surface based on the heat transfer coefficient K of the section of each enclosure surface; R=1 / u (1) K=1 / (R+Ri+Re) (2) Where R is the thermal resistance of the cross section, u is the heat transfer coefficient of the cross section, Ri is the heat transfer resistance of the inner surface of the cross section, and Re is the heat transfer resistance of the outer surface of the cross section; S5: Create a non-thick surface model in BIM, and then construct the enclosure structure of the old house based on the average heat transfer coefficient of each enclosure surface of the insulation layer model and the material of doors and windows; S6: Calculate the most economical thickness d of the insulation material using formula (3) opt : Where Pe is the energy price (yuan / kWh), ΔT is the average temperature difference between indoor and outdoor (°C), H is the number of heating / cooling hours (h / year), λ is the thermal conductivity of the insulation material (W / m·K), and P m is the unit price of insulation materials, R0 is the other thermal resistance in the old house except the insulation layer (m 2 K / W); In the enclosure structure, formula (3) is programmed to establish a calculation module in the parametric platform, and the data interface is established for each value required by formula (3). The data required by the calculation module are input to obtain the economic insulation thickness d of the insulation material. opt Input construction, then according to d opt Input structure to replace the insulation layer of old houses; S7: Install sunshades where there are windows in the old house and use a linkage control algorithm to save energy. The linkage control algorithm includes a first rule for reducing heat load by adjusting curtains, a second rule for adjusting air-conditioning temperature by PMV value, and a third rule for adjusting air-conditioning power by electricity price. The wallacei operator sets the angle and width of the sunshade as variables, and uses the comfort, energy consumption, illumination, and photovoltaic potential output by PMVPolygon, ModelToOSM, AnnualDaylight, and IncidentRadiatio as phenotypes. Through multi-objective optimization, multiple sets of optimal solutions are obtained when calculating the optimal energy saving value. Then, the width and angle of the sunshade are designed based on the multiple sets of optimal solutions, as well as the execution order of the three rules in each time period.

2. The energy consumption optimization method for old house renovation according to claim 1 is characterized in that: In step S1, an infrared scanner is used to scan the exterior walls and roof of the old house, identify the effectiveness of the insulation layer, and generate an infrared image of the insulation layer.

3. The energy consumption optimization method for old house renovation according to claim 2 is characterized in that: In step S2, the operation table of the electrical appliances in the room and the temperature and humidity settings of the air conditioner are imported into the ModelToOSM operator; at the same time, the epw meteorological file of the location of the old house is imported into the ModelToOSM operator, and the energy consumption status of the old house is simulated by the ModelToOSM operator, and the hourly energy consumption per square meter of the old house throughout the year is calculated.

4. The energy consumption optimization method for old house renovation according to claim 3 is characterized in that: In step S3, the specific steps for the BIM software to generate the insulation layer model using the black and white light and dark image are as follows: the imported black and white light and dark image is converted into image data through the image sampler operator of the BIM software to obtain a point set, and then the point set is generated into a mesh through the Conmesh operator. The mesh subdivision is adjusted to form a smooth mesh through the plug-in Weavbird, thereby forming the insulation layer model.

5. The energy consumption optimization method for old house renovation according to claim 1 is characterized in that: In step S6: Formula (3) is obtained by the following steps: The full life cycle investment cost C is calculated by formula (31) total : C total =C i +N·C e (31) Among them, C i is the initial investment cost, N is the expected payback period, C e is the annual energy consumption price; Calculate C using formula (32) e : C e =Q·P e (32) Among them, Q is the annual cooling and heating load, P e is the energy price (yuan / kWh); Q is calculated using formula (33): Where ΔT is the average temperature difference between indoor and outdoor (°C), H is the number of hours of heating / cooling (h / year), d is the thickness of the insulation layer (m), λ is the thermal conductivity of the insulation material (W / m·K), and R0 is the thermal resistance other than the insulation layer (m 2 K / W); C is calculated by formula (34) i : C i =d·P m (34) Among them, P m is the unit price of insulation materials; Combining formulas (31), (32), (33) and (34) yields formula (35): According to the economic curve of the thermal insulation layer, it can be seen that for C in formula (35), total Find the derivative, and when the derivative is 0, the most economical thickness is obtained 6. The energy consumption optimization method for old house renovation according to claim 1 is characterized in that: In step S7, the sun visor angle is set to 0-45 degrees, and the sun visor width is set to 0.5-1 meter.

7. The energy consumption optimization method for old house renovation according to claim 1 is characterized in that: In step S7, the comfort level is calculated by PMV Polygon using the enclosure structure, the epw weather file of the old house location, the linkage control algorithm, and the sunshade data; The energy consumption value is calculated by ModelToOSM using the enclosing structure, the epw weather file of the old house location, three rules, and the air conditioning operation table data; The illumination value is calculated by Annual Daylight using the enclosure structure, three rules, and the epw meteorological file data at the location of the old house; The photovoltaic potential is calculated by IncidentRadiatio using three rules and the epw meteorological file data of the location of the old house.

8. The energy consumption optimization method for old house renovation according to claim 7 is characterized in that: In step S7, the first rule includes the following steps: When the light value is greater than 500 lux, close 80% of the curtains; When the light value is 250-500 lux, close 50% of the curtains; When the light value is less than 250lux, close 20% of the curtains; The second rule includes the following steps: When the PMV is greater than 0.5, it is too hot and the air conditioner temperature is lowered by 1 degree; When the PMV is less than -0.5, it is too cold and the air conditioner temperature is increased by 1 degree; The third rule includes the following steps: When electricity prices are at their peak, the operating power of the air conditioner is reduced by 30%.

9. The energy consumption optimization method for old house renovation according to claim 8 is characterized in that: In step S7, the multi-objective optimization of the wallacei operator can also balance the optimal phenotypic parameters of illumination value and photovoltaic potential. When using photovoltaic panels, the output data of the operation is Q2 = Q1-n·Qg, where Q1 is the output data of ModelToOSM, n is the photovoltaic efficiency, and Qg is the thermal radiation received by the sunshade, its value is calculated by IncidentRadiatio.

10. The energy consumption optimization method for old house renovation according to claim 1 is characterized in that: In step S7, the sunshade is set above the south window of the old house, the sunshade is set above the east window of the old house and the side close to the south, and the sunshade is set above the west window of the old house and the side close to the south.

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