Urban building roof photovoltaic construction benefit evaluation and grading method considering supply and demand matching

Abstract

The invention discloses an urban building roof photovoltaic construction benefit evaluation and grading method considering supply and demand matching, and the method comprises the steps: carrying out the analysis based on a high-definition satellite map in combination with the specific condition of a specific urban building roof, carrying out the data integration of the high-definition satellite map through the application of artificial intelligence in machine vision, and carrying out the evaluation of the photovoltaic construction benefit of the urban building roof. And thus, roof photovoltaic construction benefit evaluation and grading in the selected area are obtained. According to the method disclosed by the invention, the available amount of the photovoltaic capacity of the urban building roof can be evaluated more accurately, more reasonable support can be provided for formulating an urban solar energy utilization scheme, the goal of realizing urban energy conservation and emission reduction is assisted, and the method has relatively high generalizability and expandability.

Description

Technical Field

[0001] The present invention belongs to the field of building technology, in particular to the design of photovoltaic construction, and specifically is a method for evaluating and grading the benefits of photovoltaic construction on urban building roofs taking into account supply and demand matching. Background Art

[0002] The large amount of greenhouse gases emitted by human activities has led to a continuous rise in global average temperatures, and the frequency of extreme weather events is also increasing. To meet this climate challenge, the carbon dioxide and greenhouse gases emitted by fossil fuels and other sources of energy must be offset by afforestation, energy conservation, and emission reduction. Furthermore, the vast number of building rooftops in cities are ideal locations for distributed solar energy systems. Therefore, promoting rooftop photovoltaic systems in cities is a key means of energy conservation and carbon reduction. However, there is currently a significant mismatch between the annual, monthly, and daily variations in solar radiation and building energy demand. The production capacity of rooftop photovoltaic equipment cannot be fully utilized. Discussions have focused on assessing the production capacity of building rooftop photovoltaic systems at the city level, assuming that all capacity can be utilized. This overestimates the amount of photovoltaic energy that can be utilized, leading to inaccurate assessments of the suitability of building rooftop photovoltaic systems.

[0003] Current computer vision technology can identify building roofs based on high-resolution satellite maps, and thus more efficiently and accurately evaluate the photovoltaic energy production of building roofs. At the same time, the increasingly mature urban building energy consumption simulation technology can effectively evaluate building energy use. Combining these two technologies in a geographic information platform to conduct supply and demand matching analysis of building roof photovoltaics and building energy use can more accurately evaluate the available amount of building roof photovoltaic production capacity, evaluate the suitability of installing photovoltaic systems on building roofs within a certain range, and avoid overestimating the benefits of roof photovoltaics.

[0004] In view of this, it is indeed necessary to combine computer vision technology and urban building energy consumption simulation technology to evaluate the available amount of building rooftop photovoltaic capacity based on the supply and demand relationship between rooftop photovoltaic capacity and building energy consumption. Then, an intuitive method for calculating and grading the construction benefits of building rooftop photovoltaic systems can be proposed, and a construction benefit grading chart can be formed to provide reasonable support for urban rooftop photovoltaic construction. In this way, solar energy resources can be efficiently utilized in urban energy transformation and help achieve the city's energy conservation and emission reduction goals. Summary of the Invention

[0005] In order to address the deficiencies of the existing technology, the present invention proposes a method for evaluating and grading the benefits of urban building rooftop photovoltaic construction, which takes supply and demand matching into consideration, to provide support for urban rooftop photovoltaic construction.

[0006] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0007] A method for evaluating and grading the benefits of photovoltaic construction on urban building roofs, taking into account supply and demand matching, comprises the following steps:

[0008] S1: Acquisition of high-resolution satellite maps: Obtain high-resolution satellite images of the study area with a spatial resolution of less than 0.6m / pixel, taken within the past three years;

[0009] S2: Satellite Image Slicing and Quality Enhancement: This process slices the acquired high-resolution satellite images into smaller images of a suitable size for deep learning model input, forming the original satellite image set. This process then corrects images with abnormal exposure, enhances contrast across all images, and reduces noise.

[0010] S3: Build a training dataset for instance segmentation model based on deep learning network:

[0011] S3-1: Image sampling and annotation: Randomly sample a certain proportion of the images obtained in S2 to form a sample image set, and then annotate each roof in the sample image set to generate the original annotated dataset;

[0012] S3-2: Data augmentation: Use data augmentation methods to expand the original annotated dataset in S3-1 and combine it with the original annotated dataset in S31 to form a dataset for training deep learning instance segmentation models;

[0013] S3-3: Dataset splitting: Split the dataset obtained in S3-2 into training set, cross-validation set and test set based on random sampling;

[0014] S4: Training an instance segmentation model based on a deep learning network: Use the dataset obtained in S3 to train an instance segmentation model based on a deep learning network;

[0015] S5: Roof instance extraction: Use the instance segmentation model obtained in S4 to extract building roof instances in all satellite images obtained in S2, and record the coordinates of each roof;

[0016] S6: Available roof photovoltaic area (S ava ) Calculation: Calculate the number of pixels that can be used for photovoltaic installation in each roof instance, and then multiply it by the spatial resolution of the satellite image in S1 to obtain S for each roof ava ;

[0017] S7: Calculation of rooftop photovoltaic installation area, installed capacity and production capacity:

[0018] S7-1: Photovoltaic module installation area (S ins ) and installed capacity (P ins ) is calculated as shown in formulas (1)-(4):

[0019] S ins =N pv ×S pv (1)

[0020] P ins =N pv ×P pv (2)

[0021]

[0022] D m =W pv ×cosβ+L m (4)

[0023] Where: N pv 、S pv and P pv Represent the number of photovoltaic modules, the area of a single module and the peak power, respectively. a and D m The distance between the front and rear rows of the photovoltaic array to prevent mutual shading, as well as the distance between the front and rear rows for maintenance, L pv and W pv are the length and width of a single photovoltaic module, β and L m is the inclination angle of the photovoltaic panels and the spacing of the pedestrian walkways for maintenance. S is obtained based on S6. ava , the photovoltaic module installation area and installed capacity of each roof can be obtained;

[0024] S7-2: The calculation method of photovoltaic production capacity (E) is shown in formula (5):

[0025] E=S ins ×R×η pv ×η sys (5)

[0026] Where R is the hourly solar radiation intensity of the study city throughout the year, η pv and η sys They are the photovoltaic cell efficiency and the system efficiency that takes into account the losses of the inverter and various devices. Combined with the calculation results in S7-1, the annual photovoltaic production capacity curve of the roof of each building in the study area can be obtained;

[0027] S8: Acquisition and integration of building energy consumption simulation data: Acquire building geometry data, including building base shape, number of floors / height, and coordinates, with a spatial resolution of no more than 1m / pixel. Acquire non-aggregate building data, including building type data and construction age data. Then, convert the above information into a unified coordinate system in the geographic information platform and use the spatial join function to associate them, completing a one-to-one correspondence between building geometry information, type, and age.

[0028] S9: Setting and calculating input parameters for urban building energy consumption simulation:

[0029] S9-1: Setting up input parameter templates for urban building energy consumption simulation: Based on building type and construction age, and by querying design standards, establish input templates for non-geometric parameters. These templates specifically include: thermal performance parameter values for the building envelope, window-to-wall ratio, room air changes / ventilation volume, indoor occupancy density, activity patterns, and daily routines, the operating schedules and power consumption of lighting and electrical equipment, and the operating logic of heating and air conditioning equipment (operating schedules, temperature settings, system type, capacity, and performance).

[0030] S9-2: Urban building energy consumption simulation input parameter template allocation and simulation calculation: Based on the type and age of each building, corresponding non-geometric parameters are assigned, and then the building performance simulation analysis software is used to calculate the hourly energy consumption of each building throughout the year;

[0031] S10: Coordinate correspondence between building rooftop PV capacity and building energy use: The coordinates of each roof instance obtained in S5 are converted to the same coordinate system as the building information in S8 and imported into geographic information software. Then, the spatial join function is used to associate all roof instances with the nearest building geometry data. The rooftop PV capacity and building energy use are aligned based on the unique ID (UID) of each building geometry data.

[0032] S11: Annual supply and demand matching calculation and building roof photovoltaic building benefit evaluation and grading:

[0033] S11-1: Calculation of the matching of building annual photovoltaic production capacity and building energy supply and demand: Based on the building UID obtained in S10, calculate the matching amount (SDM) of each building roof photovoltaic production capacity and building energy consumption, that is, the roof photovoltaic production capacity that can be utilized, as shown in formula (6):

[0034]

[0035] Where D i，t , S i，t and SDM i，t are the energy consumption, rooftop photovoltaic energy production, and available rooftop photovoltaic energy production of the i-th building within the study scope at time t, respectively;

[0036] S11-2: Calculation and classification of rooftop photovoltaic building benefits: The calculation method for the benefits of rooftop photovoltaic construction is shown in formula (7):

[0037]

[0038] Where: P iis the rooftop photovoltaic construction benefit of the i-th building within the study scope. The rooftop photovoltaic construction benefit P is defined as the potential of the building to be suitable for installing rooftop photovoltaics. It is calculated as follows: considering process matching, when no energy storage device is set, the ratio of the rooftop photovoltaic energy production available to the building body to the maximum available rooftop photovoltaic energy production (assuming all photovoltaic capacity can be utilized). The range of rooftop photovoltaic construction benefit P is [0, 1]. The larger the value, the better the matching degree between rooftop photovoltaic capacity and building energy supply and demand. That is, when no energy storage device is set and the photovoltaic capacity is used by the building body, the greater the benefit of each square meter of photovoltaic system installed. The mean-standard deviation segmentation method is used to divide the rooftop photovoltaic construction benefits of the buildings within the study scope into three levels based on the construction benefit P, as shown in Table 1. Among them, the construction benefit of level I represents that the building is most suitable for installing a rooftop photovoltaic system, and the construction benefit of level III represents that it is least suitable for installing a rooftop photovoltaic system.

[0039] Table 1 Benefit classification of photovoltaic construction on building roofs

[0040]

[0041] S12: Input the building roof photovoltaic construction benefit grading map: Based on the calculation results of S11, the construction benefits P of the buildings within the research and development scope are graded in the geographic information platform and the building roof photovoltaic construction benefit grading map is output.

[0042] Compared with the prior art, the present invention has the following advantages:

[0043] (1) This paper analyzes the supply-demand matching relationship between building rooftop photovoltaic capacity and building energy consumption to more accurately evaluate the available capacity of urban building rooftop photovoltaic capacity. Based on this, it proposes a method for evaluating and grading the benefits of urban building rooftop photovoltaic construction, providing more reasonable support for the formulation of urban solar energy utilization plans and helping to achieve the goal of urban energy conservation and emission reduction.

[0044] (2) The proposed method for evaluating the benefits of photovoltaic construction on urban building roofs normalizes the calculation results to the range [0, 1], is not affected by the solar radiation resources within the research scope, and can be used intuitively to evaluate the benefits of photovoltaic construction on roofs, with strong generalizability.

[0045] (3) The method proposed in the present invention can be directly applied to Chinese cities to fill the huge amount of vacant building roofs in Chinese cities. It has strong scalability and can provide guidance for the initial promotion of urban photovoltaic roofs, which has great application value.

[0046] (4) The present invention has strong scalability, and researchers in related fields can expand on the basis of the present invention, such as adopting new instance segmentation models, photovoltaic production capacity calculation models, building energy consumption simulation programs, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is the overall flow chart of the present invention;

[0048] Figure 2 This is a satellite image of a certain district in a certain city according to an embodiment of the present invention;

[0049] Figure 3 Schematic diagram of the installation of a rooftop photovoltaic module array according to an embodiment of the present invention (a, b);

[0050] Figure 4 The rooftop photovoltaic production capacity, building energy consumption (a), and supply-demand matching SDM curve (b) of a building on a certain day calculated within the scope of the embodiment of the present invention;

[0051] Figure 5 for Figure 2 Regional grading diagram of photovoltaic construction benefits on building roofs (a, b, c, d). DETAILED DESCRIPTION

[0052] The present invention will be further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the scope of protection of the present invention is not limited thereby.

[0053] In order to show the embodiment of the present invention more clearly and easily, the present invention selects a certain district in a certain city (district a in city A) to describe the embodiment. Figure 1 As shown, the benefit evaluation and grading method for urban building rooftop photovoltaic construction considering supply and demand matching proposed by the present invention is used to evaluate and grade the benefit of building rooftop photovoltaic construction in this embodiment. The specific method includes the following steps:

[0054] S1: Obtain high-resolution satellite maps: Use the Imagery Wayback website to obtain satellite images of the designated area a in city A (spatial resolution 0.31m / pixel), such as Figure 2 As shown;

[0055] S2: Satellite image segmentation and quality enhancement: Use the Pillow library in Python to segment the satellite image into 512×512 pixel images. Then use the cv2 library in Python to perform gamma correction on images with abnormal exposure. Then perform histogram equalization on all images.

[0056] S3: Construct a training dataset for the strength segmentation model based on a deep learning network:

[0057] S3-1: Image sampling and labeling: Randomly sample 5% of the images obtained in S2 and use LabelMe software to label each roof in the sample image set to generate the original labeled dataset:

[0058] S3-2: Data augmentation: Use the data augmentation methods in the Python imgaug library to expand the original annotated dataset in S3-1, including rotation, cloud simulation, translation, cropping, flipping, Gaussian blur, brightness enhancement, rapid snow simulation, and snowflake simulation;

[0059] S3-3: Dataset Split: Split the dataset obtained in S3-2 based on random sampling, with the training set, cross-validation set, and test set accounting for 80%, 10%, and 10% of the total dataset respectively. Use Python programming to save the three datasets in the MS COCO dataset format.

[0060] S4: Training an instance segmentation model based on a deep learning network: Build a Mask R-CNN instance segmentation model based on a deep residual network (Resnet 101) using the PyTorch library. Train the model using the dataset obtained in S3. Use stochastic gradient descent (SGD) as the optimizer, with a learning rate of 0.00025 and a momentum of 0.9. Use average precision (AP) as the performance metric for the instance segmentation model. After 50 rounds of training, the Mask R-CNN instance segmentation model based on the Resnet 101 network achieves an AP50 of 0.92 for instance segmentation on the test set.

[0061] S5: Roof instance extraction: Use the Mask R-CNN instance segmentation model trained in S4 to extract building roof instances from all satellite images obtained in S2, and record the coordinates of each roof;

[0062] S6: Calculation of rooftop photovoltaic available area (Sava): Use ImageJ software to calculate the number of pixels in each roof instance that can be used for photovoltaic installation, and then multiply it by the spatial resolution of the satellite image in S1 to obtain the Sava of each roof. ava ;

[0063] S7: Calculation of rooftop photovoltaic installation area, installed capacity and production capacity:

[0064] S7-1: The calculation method for the installation area and installed capacity of photovoltaic modules is shown in formulas (1)-(4):

[0065] S ins =N pv ×S pv (1)

[0066] P ins =N pv ×P pv (2)

[0067]

[0068] D m =W pv ×cosβ+L m (4)

[0069]

[0070] Where: Npv, Spv and Ppv represent the number of photovoltaic modules, the area of a single module and the peak power respectively; Da and Dm are the distance between the front and rear rows of the photovoltaic array to prevent mutual shading, and the distance between the front and rear rows for maintenance outflow; Lpv and Wpv are the length and width of a single photovoltaic module; β and Lm are the inclination angle of the photovoltaic module and the distance between the pedestrian walkways for maintenance, as shown in the following formula: Figure 3 Refer to GB50797-2012 "Design Specifications for Photovoltaic Power Stations", D a The calculation method is shown in formula (5), where La is the length of the inclined surface of the photovoltaic array, is the latitude of the city under study. In this example (district a of city A) is located at 31° north latitude. According to the data from the Global Solar Atlas (GSA), the optimal photovoltaic array inclination angle of city A is 25°. The photovoltaic products selected are photovoltaic modules of a certain brand. Their main performance parameters are shown in Table 1. The D of the photovoltaic array on the roof is calculated. a The roof maintenance distance L is set to 1.95m. m is 1m, combined with S6 to obtain Sav a , the installation area and capacity of photovoltaic modules on each roof can be obtained;

[0071] Table 1 Main parameters of photovoltaic modules and systems in the embodiment

[0072]

[0073] S7-2: The calculation method of photovoltaic production capacity (E) is shown in formula (6):

[0074] E=S ins ×R×η pv ×η sys (6)

[0075] Where R is the hourly solar radiation intensity on the horizontal plane throughout the year in the area where the study city is located (a typical meteorological year file was obtained from the Energy Plus website), η pv and η sys are the PV module efficiency and the system efficiency that takes into account the losses of the inverter and various devices. Combined with the calculation results in S7-1, the annual rooftop PV production capacity curve of each building in District A of City A can be obtained.

[0076] S8: Acquisition and integration of building energy consumption simulation data: Building geometry data was obtained based on Amap, Baidu Map, and OpenStreetMap (OSM) with a resolution of 1m / pixel. Building type data was combined with building classification information in OSM data and building POI (Point of Interest) data (i.e., points of interest, which refer to entities that can be abstractly understood as points in geography and contain four basic information items: name, coordinates, address, and category) obtained from the Amap API. The building type data was then reclassified into residential, office and employment, commercial service, public service, and school types. The building age data was first based on the building age information recorded in OSM data, and the accurate part was supplemented by the World Settlement Footprint Evolution (WSF) evolution data. The above information was converted to the WGS1984 coordinate system in the ArcGIS platform and linked using the spatial join function to complete the alignment of building geometry information, type, and age.

[0077] S9: Setting and calculating input parameters for urban building energy consumption simulation:

[0078] S9-1: Setting the input parameter template for urban building energy consumption simulation: Based on the building type and construction age, the input template for non-geometric parameters is established by querying the design standards. Specifically, it includes: the thermal performance parameter values of the building envelope, the window-to-wall ratio, the air changes / ventilation volume of the room, the indoor occupant density, activity patterns, and daily routines, the operating schedules and power of lighting and electrical equipment, and the operating logic of heating and air conditioning equipment (operating schedules, temperature settings, system type, capacity, and performance), etc., as shown in Table 2;

[0079] Table 2 Input parameter template and data source for urban building energy consumption simulation

[0080]

[0081] *JGJ26-95 stipulates the building envelope indicators for centralized heating in northern China, so the simulation parameters for City A were set based on the requirements of a certain city in the north (average outdoor temperature of 1-2°C during the heating season).

[0082] **JGJ26-86 sets the simulation parameters for City A based on the requirements of a city in central and western my country;

[0083] ***The setting method for public buildings in 1980 is: based on the simulated benchmark building envelope structure requirements in GB50189-2005, plus the office building work and rest schedule in this standard, and the same applies to commercial buildings.

[0084] S9-2: Urban Building Energy Consumption Simulation Input Parameter Template Allocation and Simulation Calculation: Based on the type and age of each building, corresponding non-geometric parameters are assigned. Then, the solar radiation shielding of the building is calculated using Grasshopper on the Rhino platform. Energy Plus software is used to simulate the hourly energy consumption of each building throughout the year.

[0085] S10: Coordinate correspondence between building rooftop PV capacity and building energy use: Write a Python program to convert the coordinates of each roof instance obtained in S5 to the WGS1984 coordinate system and import them into ArcGIS software. Then use the spatial join function of ArcGIS software to associate all roof instances with the nearest building geometry data. Based on the unique ID (UID) of each building geometry data, align the rooftop PV capacity and building energy use.

[0086] S11: Annual supply and demand matching calculation and building roof photovoltaic construction benefit evaluation and grading:

[0087] S11-1: Calculation of the matching of building annual photovoltaic production capacity and building energy supply and demand: Based on the building UID obtained in S10, calculate the matching amount (SDM) of each building roof photovoltaic production capacity and building energy consumption, that is, the roof photovoltaic production capacity that can be utilized, as shown in formula (7):

[0088]

[0089] Where D i，t , S i，t and SDM i，t They are the energy consumption, rooftop photovoltaic energy production, and available rooftop photovoltaic energy production of the i-th building in the study area at time t, respectively. The SDM curve of photovoltaic production capacity, building energy consumption, and supply and demand matching of a building on a certain day is as follows: Figure 4 As shown;

[0090] S11-2: Calculation and classification of rooftop photovoltaic construction benefits: The calculation method of rooftop photovoltaic construction benefits is shown in Figure (8):

[0091]

[0092] Where: P i The rooftop PV construction benefit of the i-th building within the study area is classified into three levels using the mean-standard deviation segmentation method based on the construction benefit P. Table 3 shows that level I construction benefit indicates the best match between rooftop PV production capacity and building energy consumption, making it the most suitable for rooftop PV installation. Level III construction benefit indicates the least suitable for rooftop PV installation.

[0093] Table 3 Benefit classification of photovoltaic construction on building roofs

[0094]

[0095] S12: Output building roof photovoltaic construction benefit classification map: Based on the calculation results of S11, the construction benefit P of the buildings in District A of City A is classified in ArcGIS, such as Figure 5 Specifically, in this embodiment, photovoltaic systems should be installed on the roofs of buildings with a Class I construction benefit. If the installed photovoltaic capacity of buildings with a Class I construction benefit cannot meet the installed capacity target of this embodiment, then buildings with a Class II construction benefit should be selected for installation, and so on.

Claims

1. A method for evaluating and grading the benefits of photovoltaic construction on urban building roofs, taking into account supply and demand matching, characterized by: The specific steps include: S1: high-resolution satellite map acquisition; S2: Satellite Image Slicing and Quality Enhancement: This process slices the acquired high-resolution satellite images into smaller images of a suitable size for deep learning model input, forming the original satellite image set. This process then corrects images with abnormal exposure, enhances contrast across all images, and reduces noise. S3: Build a training dataset for an instance segmentation model based on a deep learning network: Sample and annotate rooftops from the images obtained in S2, use data augmentation techniques to increase the data capacity, and then split the augmented dataset into a training set, a cross-validation set, and a test set. S4: Training instance segmentation models based on deep learning networks; S5: Roof instance extraction: Use the instance segmentation model obtained in S4 to extract building roof instances in all satellite images obtained in S2, and record the coordinates of each roof; S6: Available roof photovoltaic area (S ava ) Calculation: Calculate the number of pixels that can be used for photovoltaic installation in each roof instance, and then multiply it by the spatial resolution of the satellite image in S1 to obtain S for each roof ava ; S7: Calculation of rooftop photovoltaic installation area, installed capacity and production capacity; S8: Acquisition and integration of building energy consumption simulation data; S9: Setting and calculating input parameters for urban building energy consumption simulation; S10: Coordinate correspondence between building rooftop photovoltaic capacity and building energy consumption results; S11: Annual supply and demand matching calculation and building roof photovoltaic construction benefit evaluation and grading; S12: Output building roof photovoltaic construction benefit grading map: Based on the calculation results of S11, the construction benefits P of the buildings within the research scope are graded in the geographic information platform and the building roof photovoltaic construction benefit grading map is output.

2. The evaluation and grading method according to claim 1, wherein: The spatial resolution of the high-resolution satellite map in S1 is required to be less than 0.6m / pixel, and the shooting time is within the last three years.

3. The evaluation and grading method according to claim 1, wherein: The calculation of rooftop photovoltaic installation area, installed capacity and production capacity in S7 includes: S7-1: Photovoltaic module installation area (S ins ) and installed capacity (P ins ) is calculated as shown in formulas (1)-(4): S ins =N pv ×S pv (1) P ins =N pv ×P pv (2) D m =W pv ×cosβ+L m (4) Where: N pv 、S pv and P pv Represent the number of photovoltaic modules, the area of a single module and the peak power, respectively. a and D m The distance between the front and rear rows of the photovoltaic array to prevent mutual shading, as well as the distance between the front and rear rows for maintenance, L pv and W pv are the length and width of a single photovoltaic module, β and L m is the inclination angle of the photovoltaic panels and the spacing of the pedestrian walkways for maintenance. S is obtained based on S6. ava , the photovoltaic module installation area and installed capacity of each roof can be obtained; S7-2: The calculation method of photovoltaic production capacity (E) is shown in formula (5): E=S ins ×R×η pv ×η sys (5) Where R is the hourly solar radiation intensity of the study city throughout the year, η pv and η sys They are respectively the photovoltaic cell efficiency and the system efficiency which comprehensively considers the losses of the inverter and various equipment. Combined with the calculation results in S7-1, the annual photovoltaic production capacity curve of the roof of each building in the study scope can be obtained.

4. The evaluation and grading method according to claim 1, wherein: The acquisition and integration of building energy consumption simulation data in S8 includes: acquiring building geometric data, including building base shape, number of floors / height, and coordinates, with a spatial resolution of no more than 1m / pixel; acquiring non-aggregate building data, including building type data and building age data; and then converting the above information into a unified coordinate system in a geographic information platform, and using a spatial connection function to associate them, thereby completing a one-to-one correspondence between building geometric information, type, and age.

5. The evaluation and grading method according to claim 1, wherein: The setting and calculation of the input parameters for the urban building energy consumption simulation in S9 include: S9-1: Setting up input parameter templates for urban building energy consumption simulation: Based on building type and construction age, and by querying design standards, establish input templates for non-geometric parameters. These templates specifically include: thermal performance parameter values for the building envelope, window-to-wall ratio, room air changes / ventilation volume, indoor occupancy density, activity patterns, and daily routines, the operating schedules and power consumption of lighting and electrical equipment, and the operating logic of heating and air conditioning equipment (operating schedules, temperature settings, system type, capacity, and performance). S9-2: Urban building energy consumption simulation input parameter template allocation and simulation calculation: Based on the type and age of each building, corresponding non-geometric parameters are assigned, and then the building performance simulation analysis software is used to calculate the hourly energy consumption of each building throughout the year.

6. The evaluation and grading method according to claim 1, wherein: The coordinate correspondence between the building roof photovoltaic capacity and building energy consumption results in S10 includes: converting the coordinates of each roof instance obtained in S5 into the same coordinate system as the building information in S8, and importing it into the geographic information software, and then using the spatial connection function to associate all roof instances with the nearest building geometric data, and aligning the roof photovoltaic capacity and building energy consumption based on the unique number (UID) of each building geometric data.

7. The evaluation and grading method according to claim 1, wherein: The annual supply and demand matching calculation and building rooftop photovoltaic construction benefit evaluation classification in S11 include: S11-1: Calculation of the matching of building annual photovoltaic production capacity and building energy supply and demand: Based on the building UID obtained in S10, calculate the matching amount (SDM) of each building roof photovoltaic production capacity and building energy consumption, that is, the roof photovoltaic production capacity that can be utilized, as shown in formula (6): Where D i,t , S i,t and SDM i,t are the energy consumption, rooftop photovoltaic energy production, and available rooftop photovoltaic energy production of the i-th building within the study scope at time t, respectively; S11-2: Calculation and classification of rooftop photovoltaic building benefits: The calculation method for the benefits of rooftop photovoltaic construction is shown in formula (7): Where: P i is the rooftop photovoltaic construction benefit of the i-th building within the study scope. The rooftop photovoltaic construction benefit P is defined as the potential of the building to be suitable for installing rooftop photovoltaics. It is calculated as follows: considering process matching, when no energy storage device is set, the ratio of the rooftop photovoltaic production capacity available to the building body to the maximum available rooftop photovoltaic production capacity. The range of rooftop photovoltaic construction benefit P is [0,1]. The larger the value, the better the matching degree between the rooftop photovoltaic production capacity and the supply and demand of building energy consumption. That is, when no energy storage device is set and the photovoltaic production capacity is used by the building body, the greater the benefit of each square meter of photovoltaic system installed. The mean-standard deviation segmentation method is used to divide the rooftop photovoltaic construction benefits of the buildings within the study scope into three levels based on the construction benefit P. Among them, the construction benefit of level I represents that the building is most suitable for installing a rooftop photovoltaic system, and the construction benefit of level III represents that it is least suitable for installing a rooftop photovoltaic system.

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