Urban solar energy potential assessment method based on building individual visual angle

Abstract

The invention discloses an urban solar energy potential assessment method based on an individual visual angle of a building. The method comprises the following steps: acquiring surface temperature data, air quality data, a digital elevation model, cloud cover data, sunshine duration data and building footprint data; constructing an illumination intensity index based on the surface temperature data, the air quality data and the digital elevation model; on the basis of the cloud cover data, combining with the sunshine duration data to construct an effective illumination duration index; based on the building footprint data, the illumination intensity index and the effective illumination duration index are combined, the solar energy potential evaluation index of the individual building is calculated, and the higher the solar energy potential evaluation index of the individual building is, the larger the solar energy potential of the city is. The method is high in calculation efficiency and accurate in evaluation result, provides a reference index for solar energy potential of buildings in various regions, has real-time performance and dynamic performance, provides powerful guidance for effective utilization of intermittent solar energy, indicates a direction for comprehensive application of energy, and reduces unnecessary fund and manpower waste.

Description

Technical Field

[0001] This invention relates to the technical field of geographic information systems, and more particularly to a method for assessing urban solar energy potential. Background Technology

[0002] In today's world, with the growing global energy crisis and environmental pollution, promoting the application of renewable energy has become a consensus among governments and societies worldwide. Solar energy, as a clean, green, and virtually inexhaustible energy resource, is playing an increasingly important role in the energy sector. Especially in the building sector, the use of solar energy not only reduces building energy consumption and greenhouse gas emissions but also helps improve energy efficiency and promote energy conservation and emission reduction.

[0003] Globally, the distribution of solar energy resources is significantly influenced by factors such as latitude, climate, and season. Generally, regions near the equator experience stronger solar radiation and longer average annual sunshine hours, resulting in abundant solar energy resources. For example, most of Africa, the Middle East, and the equatorial regions of South America possess exceptional solar energy potential. Conversely, high-latitude regions, especially the Arctic and Antarctic, have relatively lower solar energy resources due to extremely short winter sunshine hours.

[0004] China is a vast region stretching from east to west and from north to south, resulting in significant differences in the distribution of its solar energy resources. Western Region: The western region, especially the northwest, including Gansu, Ningxia, Qinghai, and the Xinjiang Uyghur Autonomous Region, is located in China's arid and semi-arid areas. It boasts high solar radiation intensity, long annual sunshine hours, and enormous solar energy potential. The Xinjiang Uyghur Autonomous Region, in particular, possesses extremely rich solar energy resources and is one of the ideal regions for solar power generation globally. Eastern Region: Although the eastern region is influenced by the monsoon climate and has shorter overall sunshine hours, it still possesses relatively abundant solar energy resources. For example, provinces such as Jiangsu, Zhejiang, and Fujian, while having less sunshine conditions compared to the west, still have considerable solar power generation potential. Southern Region: Southern regions such as Guangdong, Hainan, and Guangxi have a warm and humid climate with moderate annual sunshine hours, and their solar energy resources also have considerable utilization potential. Hainan Island, in particular, has ample sunshine throughout the year, making its solar energy utilization potential very large. Northern regions: Northern regions such as Beijing, Hebei, and Inner Mongolia have shorter daylight hours in winter, but strong sunlight in summer, so there is also potential for solar energy utilization.

[0005] According to the International Energy Agency (IEA), approximately 50% of global solar radiation is concentrated in regions near the equator, while solar radiation intensity is significantly lower in areas closer to the poles. Therefore, the potential of solar energy varies considerably across the globe and requires assessment and planning based on specific regional climate conditions and sunlight levels.

[0006] To maximize the utilization of solar energy in buildings, solar energy potential assessment has become a crucial research topic. A precise and scientific solar energy potential assessment system can provide architects, planners, and owners with a deep understanding of a building's solar power generation potential, helping them make informed decisions based on actual needs and environmental conditions. Through scientific assessment, building design can be optimized, and the placement of solar energy equipment can be more rational, further improving energy efficiency and minimizing energy consumption. Furthermore, the solar energy potential assessment system provides a scientific basis for energy-saving renovations of buildings, offering practical solutions for improving the green energy level of buildings, reducing carbon emissions, and achieving sustainable development goals. The implementation of this assessment system not only helps improve the overall energy efficiency of buildings but also makes a positive contribution to the transformation of urban energy structures and the promotion of green building standards.

[0007] Traditional illumination analysis methods, such as those based on building models or assessment models that integrate factors such as weather, building shape, and shading effects, often suffer from limited assessment accuracy and low computational efficiency. They also fall short in terms of personalized analysis, lacking real-time performance and dynamic adaptability, making it difficult to meet the complex and ever-changing assessment needs of today.

[0008] Patent application number 202010493278.2 discloses a method and system for assessing the usable solar energy resources of urban building complexes. The method includes: acquiring solar position information; acquiring geometric and orientation information of buildings; acquiring roof and side facade data of buildings; and calculating the shading time at different locations on the roof and side facades of buildings based on the solar position information, the relative positions and geometric relationships between buildings, and the roof and side facade data. This allows for the acquisition of the total solar radiation received by the roof and side facades and the total global solar radiation within a preset time period, thus accurately assessing the solar energy utilization potential of the roof and side facades of a building complex and providing scientific decision-making support for the development and utilization of solar energy resources in urban building complexes. However, the above invention is based on traditional illumination analysis methods that consider building models, building shapes, and shading effects. These methods require detailed calculations on individual buildings, resulting in complex and inefficient formulas. Furthermore, assessing the solar energy resource potential of a city's building complex requires significant human, material, and financial resources. It also lacks dynamism and real-time capability. After calculating the solar potential of a building complex, it cannot conduct real-time assessments, failing to meet the assessment needs caused by the variability of climate change. Furthermore, it cannot conduct large-scale assessments of the solar potential of building complexes; due to inherent methodological limitations, the workload is too cumbersome, limiting it to small-scale assessments and failing to meet personalized analysis requirements. Summary of the Invention

[0009] This invention proposes a method for assessing urban solar energy potential from the perspective of individual buildings, aiming to solve the technical problems of limited assessment accuracy, low computational efficiency, insufficient personalized analysis, and lack of real-time and dynamic capabilities of traditional methods. This will provide better technical reference and data support for the allocation and installation optimization of urban photovoltaic resources in my country.

[0010] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0011] A method for assessing urban solar energy potential from the perspective of individual buildings includes the following steps:

[0012] S0. Acquire multi-source spatiotemporal big data, including surface temperature data, air quality data, digital elevation models, cloud cover data, sunshine duration data, and building footprint data;

[0013] S1. Based on surface temperature data, air quality data, and digital elevation model, construct the light intensity index; based on cloud cover data and sunshine duration data, construct the effective sunshine duration index.

[0014] S2. Based on building footprint data, combined with the light intensity index and effective sunshine duration index, calculate the solar energy potential assessment index for individual buildings. Based on the solar energy potential assessment index of individual buildings, measure the solar energy potential of the city and complete the city's solar energy potential assessment.

[0015] The method for constructing the light intensity index is as follows: PM2.5 concentration data is used as air quality data, and the surface temperature data, air quality data, and remote sensing data of the digital elevation model are normalized to obtain the surface temperature index, the digital elevation model index, and the air pollution index; based on the surface temperature index, the digital elevation model index, and the air pollution index, the light intensity index is obtained.

[0016] The expressions for the surface temperature index, digital elevation model index, and air pollution index are as follows:

[0017] ;

[0018] ;

[0019] ;

[0020] Where LST is the surface temperature, LST index It is the land surface temperature index, LST max It is the maximum value of the surface temperature data, LST min It represents the minimum surface temperature data; DEM is a digital height model. index It is the Digital Elevation Model Index, DEM maxIt is the maximum value of the digital elevation model (DEM) data. min This refers to the minimum value in the digital elevation model data; PM2.5 is air pollution data. index It's the air pollution index, PM2.5. max This is the maximum value of air pollution data, PM2.5. min It is the minimum value of air pollution data.

[0021] The light intensity index .

[0022] The method for constructing the effective sunshine duration index is as follows: calculate the cloud cover density index based on cloud cover data; classify the weather into very sunny, generally sunny, cloudy or overcast based on the cloud cover density index, and determine the sunshine duration adjustment coefficient; normalize the sunshine duration data to obtain the sunshine duration index; and obtain the effective sunshine duration index based on the sunshine duration adjustment coefficient and the sunshine duration index.

[0023] The expression for calculating the cloud cover density index based on cloud cover data is as follows:

[0024] ;

[0025] ;

[0026] In the formula, UCD index It is the cloud density index, UCD max It is the maximum value of cloud density data, UCD min It represents the minimum cloud density data; UCD is the percentage of cloud coverage area in a city; CCD... area It is the city's cloud cover index, CAD. area It is the city's area index.

[0027] The expression for the illumination duration adjustment coefficient is:

[0028] In the formula, T is the sunshine duration adjustment coefficient. When the sunshine duration adjustment coefficient T is 0.7, it indicates that the weather is very sunny; when the sunshine duration adjustment coefficient T is 0.5, it indicates that the weather is generally sunny; when the sunshine duration adjustment coefficient T is 0.4, it indicates that the weather is cloudy; and when the sunshine duration adjustment coefficient T is 0.3, it indicates that the weather is overcast.

[0029] The sunshine duration index Among them, SSD max This is the maximum value of sunshine duration data, SSD min It represents the minimum sunshine duration data, SSD is the sunshine duration; Effective Sunshine Duration Index .

[0030] The method for calculating the solar energy potential assessment index of an individual building is as follows: a city solar energy potential index is constructed based on the solar intensity index and the effective sunshine duration index; the individual building area is calculated based on building footprint data, and combined with the city solar energy potential index, the individual building solar energy potential assessment index is obtained.

[0031] The expression for the city's solar energy potential index is:

[0032] ;

[0033] In the formula, SEP index It is the city's solar energy potential index;

[0034] The expression for the solar energy potential assessment index of an individual building is:

[0035] ;

[0036] in, SPI is the Solar Energy Potential Assessment Index for Individual Buildings, BFP index It is the roof area index of individual buildings, SEP index It is the city's solar energy potential index. It is an original individual building solar energy potential assessment index. It is the maximum value of the original individual building's solar energy potential assessment index. It is the minimum value of the original individual building's solar energy potential assessment index.

[0037] The higher the solar energy potential assessment index of an individual building, the greater the solar energy potential of the city.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] This invention employs multi-source spatiotemporal data fusion technology, integrating multiple data sources such as remote sensing technology, geographic information systems (GIS), meteorological data, and building models. It uses data fusion algorithms to comprehensively assess the solar energy potential of buildings, fully considering the complex relationship between buildings and the environment, and improving the accuracy of the assessment results.

[0040] By utilizing real-time updated remote sensing imagery and comprehensively considering various data sources, including surface temperature, air quality indicators primarily based on PM2.5 concentration, digital elevation models, cloud cover, sunshine duration, and building footprint, the solar energy potential of individual buildings was assessed, significantly improving computational efficiency. Thanks to the real-time updates of satellite data, the temporal accuracy of the assessment ensured the immediate updating of the solar energy potential assessment index. Simultaneously, spatial accuracy reached the individual building level, achieving a solar energy potential assessment index with a resolution of 0.5 meters.

[0041] This invention provides a valuable reference index for the solar energy potential of buildings in various regions. It boasts high computational efficiency, accurate assessment results, and the ability to perform personalized analysis from an individual building perspective. Because it is based on remote sensing imagery and transmitted in real-time via satellite data, it ensures the real-time and dynamic nature of the individual building's solar energy potential assessment index. It provides strong guidance for the effective utilization of intermittent solar energy, points the way for comprehensive energy application, and effectively avoids unnecessary waste of funds and manpower.

[0042] High assessment accuracy: This invention relies on satellite remote sensing product data. As the accuracy of remote sensing product data continues to improve, the assessment accuracy of this invention will also improve accordingly.

[0043] High computational efficiency: The calculation formula of this invention is not complicated but considers many factors, resulting in accurate evaluation results and higher computational efficiency.

[0044] Personalized Analysis: Traditional solar radiation assessment models struggle to provide precise evaluations of individual buildings when dealing with large areas. In contrast, this invention, based on building footprint data, can provide a personalized solar potential index assessment for each building, achieving a more detailed evaluation from the building's perspective.

[0045] Real-time and dynamic: Thanks to the real-time updating and dynamic characteristics of satellite remote sensing data, the evaluation results of this invention are real-time and dynamic.

[0046] Large-scale assessment: Traditional methods for assessing solar radiation intensity are often limited to small-scale assessments to achieve personalized analysis, but are difficult to extend to provincial, national, or even world-level scales due to limitations in computational efficiency and workload. This invention, however, enables large-scale solar potential assessments from a building-centric perspective. Currently, this invention has been successfully used to complete national-level assessments in China, and world-level assessments are also feasible and implementable. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is the overall technical roadmap for evaluating the solar energy potential index of individual buildings according to the present invention.

[0049] Figure 2This is an example of the calculation result of the solar energy potential assessment index for an individual building according to an embodiment of the present invention, wherein, Figure 2 - (a) shows the calculation results of the solar energy potential assessment index for individual buildings in Urumqi. Figure 2 - (b) Calculation results of the solar energy potential assessment index for individual buildings in Lhasa. Figure 2 - (c) shows the calculation results of the solar energy potential assessment index for individual buildings in Zhengzhou. Figure 2 - (d) represents the calculated solar energy potential assessment index for individual buildings in Guangzhou.

[0050] Figure 3 This invention provides a comparison of the correlation between the solar energy potential index of different regions in my country and existing products, representing an embodiment of the present invention.

[0051] Figure 4 This is an example of partial results for calculating the solar energy potential assessment index of individual buildings in different regions and seasons according to an embodiment of the present invention.

[0052] The map of China involved in the image has the map approval number GS(2024)0650 and the coordinate system is GCS_WGS_1984. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Example 1

[0055] like Figure 1 As shown, a method for assessing urban solar energy potential from the perspective of individual buildings is proposed. This method processes multi-source spatiotemporal big data using geographic information technology to construct an urban solar energy potential assessment index based on the individual building perspective, reflecting the differences in urban solar energy potential among different regions in my country. The method includes the following steps:

[0056] S0. Acquire multi-source spatiotemporal big data, including surface temperature data, air quality data, digital elevation model (DEM), cloud cover data, sunshine duration data, and building footprint data.

[0057] Furthermore, in this embodiment, PM2.5 concentration data is used as air quality data; both surface temperature data and PM2.5 concentration data fall under the category of remote sensing products, acquired from the National Tibetan Plateau Data Center, covering the entire country, and presented in raster data form. Digital elevation model data originates from the Geospatial Data Cloud and is also used as raster data at a national scale. Cloud cover data is sourced from NEO (NASA Earth Observations), providing monthly cloud cover data. Sunshine duration data is generated by processing the China Surface Climate Data Daily Value Dataset V3.0, specifically monthly sunshine duration data. Building footprint data comes from the ZENODO platform and consists of 0.5-meter resolution building vector data covering the entire country.

[0058] S1. Taking into account both natural conditions and the impact of human activities, a light intensity index is constructed based on surface temperature data, air quality data, and a digital elevation model; an effective sunshine duration index is constructed based on cloud cover data and sunshine duration data.

[0059] Solar radiation intensity is significantly influenced by a variety of factors, including latitude, topography, and air pollution levels. Specifically, solar radiation intensity is typically higher at lower latitudes, higher altitudes, and lower levels of air pollution. Furthermore, there is a clear negative correlation between surface temperature and latitude; that is, surface temperature tends to decrease as latitude increases. Therefore, this invention selects surface temperature as a proxy indicator reflecting the impact of latitude on solar radiation intensity.

[0060] The method for constructing the light intensity index is as follows:

[0061] The surface temperature data, air quality data, and remote sensing data from the digital elevation model were normalized to ensure that the data range was within [0, 1], thus eliminating the influence of different dimensions of the remote sensing data on the index calculation results. Among them, surface temperature and topographic elevation are positively correlated with solar radiation intensity, while PM2.5 concentration is negatively correlated with solar radiation intensity.

[0062] The normalization expression is shown below:

[0063] (1)

[0064] (2)

[0065] (3)

[0066] In the formula, LST is the surface temperature. index It is the land surface temperature index, LST max It is the maximum value of the surface temperature data, LST minIt represents the minimum surface temperature data; DEM is a digital height model. index It is the Digital Elevation Model Index, DEM max It is the maximum value of the digital elevation model (DEM) data. min This refers to the minimum value in the digital elevation model data; PM2.5 is air pollution data. index It's the air pollution index, PM2.5. max This is the maximum value of air pollution data, PM2.5. min It is the minimum value of air pollution data.

[0067] Taking into account the effects of latitude, topography, and air pollution on solar radiation intensity, a light intensity index (LI) is constructed to reflect the spatial differences in solar radiation intensity among different regions of my country. The specific calculation formula is shown below:

[0068] (4)

[0069] Fluctuations caused by weather changes and rapid cloud movement result in varying degrees of intermittency in solar radiation, thus affecting the duration of effective sunshine in different areas. Therefore, cloud cover has become an important and reliable indicator of weather conditions. Generally, the clearer the weather, the less cloud cover, and the longer the effective sunshine duration; conversely, increased cloud cover often indicates worsening weather conditions, with higher cloud density indicating more severe weather and reduced effective sunshine duration. Therefore, cloud cover density is calculated for each city to characterize its weather conditions.

[0070] With the availability of cloud cover data (CCD), weather conditions are characterized by changes in cloud cover, and effective sunshine duration data (ESD) is constructed by combining sunshine duration data.

[0071] The formula for calculating the cloud density index is as follows:

[0072] (5)

[0073] (6)

[0074] In the formula, UCD index It is the cloud density index, UCD max It is the maximum value of cloud density data, UCD min It represents the minimum cloud density data; UCD is the percentage of cloud coverage area in a city; CCD... area It is the city's cloud cover index, CAD.area It is the city's area index.

[0075] Different weather conditions have varying effects on effective sunshine duration; the worse the weather, the shorter the effective sunshine duration. Based on cloud cover density index, weather conditions are categorized as very sunny, generally sunny, cloudy, and overcast. By consulting literature, sunshine duration adjustment coefficients were determined (very sunny: 0.7, generally sunny: 0.5, cloudy: 0.4, overcast: 0.3), and the specific calculation formulas are shown below:

[0076] (7)

[0077] In the formula, T is the illumination duration adjustment coefficient (transmittance).

[0078] Normalize the sunshine duration data so that the data range is [0, 1]. The specific normalization formula is shown below:

[0079] (8)

[0080] In the formula, SSD index It is the sunshine duration index, SSD max This is the maximum value of sunshine duration data, SSD min It represents the minimum sunshine duration data, and SSD represents the sunshine duration.

[0081] The sunshine duration data is adjusted using a sunshine duration adjustment factor to obtain the Effective Sunshine Duration Data (ESD). The specific calculation formula is shown below:

[0082] (9)

[0083] In the formula, ESD is the effective sunshine duration index.

[0084] S2. The release of high-resolution building footprint data (BFP) helps reveal spatial differences in the solar energy potential of individual buildings in a city. Based on the building footprint data, combined with the illuminance index and effective sunshine duration index, the Solar Energy Potential Assessment Index (SPI) is calculated for each individual building. The SPI measures the solar energy potential of a city; specifically, the higher the SPI, the greater the solar energy potential and the better the performance of the city.

[0085] S21. Based on the solar irradiance index and effective sunshine duration index, construct an urban solar energy potential index to reflect the changes in solar energy potential in different urban areas. The specific calculation formula is shown below:

[0086] (10)

[0087] In the formula, SEP index It is the city's solar energy potential index.

[0088] S22. The larger the building, the larger the area that can be fitted with photovoltaics, and the higher the building's solar energy potential. Based on high-resolution building footprint data (BFP), the individual building area is calculated, and combined with the city's solar energy potential index, it is correlated with the individual building to construct an individual building solar energy potential assessment index. The specific calculation formula is shown below:

[0089] (11)

[0090] (12)

[0091] In the formula, SPI is the solar energy potential assessment index for individual buildings, and BFP is... index It is the roof area index of individual buildings, SEP index It is the city's solar energy potential index. It is an original individual building solar energy potential assessment index. It is the maximum value of the original individual building's solar energy potential assessment index. It is the minimum value of the original individual building's solar energy potential assessment index.

[0092] Example 2

[0093] Based on surface temperature data, air quality data, and a digital elevation model, a solar radiation intensity index was constructed to characterize the impact of latitude, topography, and air pollution on solar radiation intensity. The calculated solar radiation intensity indices for Xinjiang Uygur Autonomous Region, Tibet Autonomous Region, Henan Province, and Guangdong Province in 2020 clearly show local spatial differences in solar radiation intensity across different provinces. Solar radiation intensity is generally high in Xinjiang Uygur Autonomous Region and Guangdong Province; in Henan Province, solar radiation intensity is high in the south and low in the north; and in Tibet Autonomous Region, solar radiation intensity exhibits significant heterogeneity due to topographical conditions.

[0094] The data used to construct the solar irradiance index is remote sensing data, which can fully display the local spatial variation of solar irradiance within a city. The cloud cover and sunshine duration data used to construct the solar effective sunshine duration index are both shapefile (SHP) data with prefecture-level cities as the basic statistical unit. While these cannot reflect the variation of effective sunshine duration within a city, the variation of effective sunshine duration between urban areas is clearly visible. Considering that weather conditions are generally homogeneous within a local spatial range (e.g., urban areas), the variation of effective sunshine duration within a city can be ignored. The variation of effective sunshine duration in Xinjiang Uygur Autonomous Region, Tibet Autonomous Region, Henan Province, and Guangdong Province in 2020 is as follows: In Xinjiang Uygur Autonomous Region, effective sunshine duration generally shows a trend of lower in the west and higher in the east; in Henan Province, effective sunshine duration generally shows a trend of lower in the south and higher in the north; in Tibet Autonomous Region, effective sunshine duration generally shows a trend of higher in the west and lower in the east; and in Guangdong Province, due to drastic weather changes, effective sunshine duration shows irregular variations.

[0095] Based on building footprint data, combined with solar irradiance index and effective sunshine duration index, an individual building solar energy potential assessment index is constructed to reflect the individual variations in building solar energy potential across regions. The calculation results are as follows: Figure 2 As shown, the solar irradiance index and effective sunshine duration index are spatially linked to the building footprint data. Larger building areas correspond to higher solar irradiance and longer effective sunshine duration, resulting in more solar energy received by the photovoltaic system and thus greater solar energy potential for individual buildings. The distribution of buildings and changes in solar energy potential are clearly visible in the graph, providing technical reference and data support for the rational allocation and installation optimization of photovoltaic resources. However, due to the province-wide scope, the details of individual buildings cannot be well displayed, and the high density of buildings leads to poor map visualization. Therefore, Urumqi, Lhasa, Zhengzhou, and Guangzhou are used as examples for illustration.

[0096] To verify the validity of the calculation results of this invention, the building footprint was segmented, the solar energy potential index was extracted, and it was compared with existing solar radiation product data by region, such as... Figure 3 As shown in the figure, in provinces such as Guizhou, Shaanxi, and Guangxi, the results calculated using this invention show a good correlation with existing products. In these regions, solar energy changes are mainly affected by natural conditions, thus demonstrating the reliability of the calculation results of this invention. However, in provinces such as Jiangxi and Anhui, the correlation is lower because the weather changes are more drastic, affected by rainfall, resulting in a lower correlation. This also indicates that existing products do not consider the impact of weather changes on solar radiation. This invention adjusts the effective sunshine duration by cloud cover data, which can effectively reflect changes in solar radiation.

[0097] Due to the high density of buildings and the vastness of the cities, the SPI (Spiritual Infrastructure) display from the aforementioned urban perspective was not ideal. Therefore, selected areas in Beijing, Zhengzhou, Wuhan, and Guangzhou were chosen for display to achieve better results. Figure 4 As shown, February, May, August, and November are used to represent the four seasons. Beijing, Zhengzhou, Wuhan, and Guangzhou are selected from south to north latitude to represent the SPI (Solar Energy Potential) trends in China, both latitudinally and seasonally. The figures clearly show the changes in the solar energy potential of individual buildings in different cities and seasons. By analyzing the spatial differentiation and seasonal variation characteristics of the solar energy potential assessment index of individual buildings in Chinese cities calculated using the method of this invention, key decision-making basis and support are provided for the rational allocation of photovoltaic resources within my country and the optimization of photovoltaic facility installation in cities.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing urban solar energy potential from the perspective of individual buildings, characterized in that, Includes the following steps: S0. Acquire multi-source spatiotemporal big data, including surface temperature data, air quality data, digital elevation models, cloud cover data, sunshine duration data, and building footprint data; S1. Construct a light intensity index based on surface temperature data, air quality data, and digital elevation model; Based on cloud data and combined with sunshine duration data, an effective sunshine duration index is constructed. S2. Based on building footprint data, combined with the light intensity index and effective sunshine duration index, calculate the solar energy potential assessment index for individual buildings. Based on the solar energy potential assessment index of individual buildings, measure the solar energy potential of the city and complete the city's solar energy potential assessment.

2. The method for assessing urban solar energy potential based on the perspective of individual buildings as described in claim 1, characterized in that, The method for constructing the light intensity index is as follows: PM2.5 concentration data is used as air quality data, and the surface temperature data, air quality data, and remote sensing data of the digital elevation model are normalized to obtain the surface temperature index, the digital elevation model index, and the air pollution index; based on the surface temperature index, the digital elevation model index, and the air pollution index, the light intensity index is obtained.

3. The method for assessing urban solar energy potential based on the perspective of individual buildings as described in claim 2, characterized in that, The expressions for the surface temperature index, digital elevation model index, and air pollution index are as follows: ； ； ； Where LST is the surface temperature, LST index It is the land surface temperature index, LST max It is the maximum value of the surface temperature data, LST min It represents the minimum surface temperature data; DEM is a digital height model. index It is the Digital Elevation Model Index, DEM max It is the maximum value of the digital elevation model (DEM) data. min This refers to the minimum value in the digital elevation model data; PM2.5 is air pollution data. index It's the air pollution index, PM2.

5. max This is the maximum value of air pollution data, PM2.

5. min It is the minimum value of air pollution data.

4. The method for assessing urban solar energy potential from the perspective of individual buildings, as described in claim 2 or 3, is characterized in that... The light intensity index .

5. The method for assessing urban solar energy potential based on the perspective of individual buildings according to claim 4, characterized in that, The method for constructing the effective sunshine duration index is as follows: calculate the cloud cover density index based on cloud cover data; classify the weather into very sunny, generally sunny, cloudy or overcast based on the cloud cover density index, and determine the sunshine duration adjustment coefficient; normalize the sunshine duration data to obtain the sunshine duration index. The effective sunshine duration index is obtained based on the sunshine duration adjustment coefficient and the sunshine duration index.

6. The method for assessing urban solar energy potential based on the perspective of individual buildings, as described in claim 5, is characterized in that... The expression for calculating the cloud cover density index based on cloud cover data is as follows: ； ； In the formula, UCD index It is the cloud density index, UCD max It is the maximum value of cloud density data, UCD min It represents the minimum cloud density data; UCD is the percentage of cloud coverage area in a city; CCD... area It is the city's cloud cover index, CAD. area It is the city's area index.

7. The method for assessing urban solar energy potential based on the perspective of individual buildings, as described in claim 5 or 6, is characterized in that... The expression for the illumination duration adjustment coefficient is: In the formula, T is the sunshine duration adjustment coefficient. When the sunshine duration adjustment coefficient T is 0.7, it indicates that the weather is very sunny; when the sunshine duration adjustment coefficient T is 0.5, it indicates that the weather is generally sunny; and when the sunshine duration adjustment coefficient T is 0.4, it indicates that the weather is cloudy. When the sunshine duration adjustment coefficient T is 0.3, it indicates that the weather is cloudy.

8. The method for assessing urban solar energy potential based on the perspective of individual buildings, as described in claim 7, is characterized in that... The sunshine duration index Among them, SSD max This is the maximum value of sunshine duration data, SSD min It represents the minimum sunshine duration data, SSD is the sunshine duration; Effective Sunshine Duration Index .

9. The method for assessing urban solar energy potential based on the perspective of individual buildings, as described in claim 8, is characterized in that... The method for calculating the solar energy potential assessment index of individual buildings is as follows: a city solar energy potential index is constructed based on the solar irradiance index and the effective sunshine duration index. Based on building footprint data, the individual building area is calculated, and combined with the city's solar energy potential index, an individual building solar energy potential assessment index is obtained.

10. The method for assessing urban solar energy potential based on the perspective of individual buildings, as described in claim 9, is characterized in that... The expression for the city's solar energy potential index is: ； In the formula, SEP index It is the city's solar energy potential index; The expression for the solar energy potential assessment index of an individual building is: ； in, SPI is the Solar Energy Potential Assessment Index for Individual Buildings, BFP index It is the roof area index of individual buildings, SEP index It is the city's solar energy potential index. It is an original individual building solar energy potential assessment index. It is the maximum value of the original individual building's solar energy potential assessment index. It is the minimum value of the original individual building's solar energy potential assessment index.

Images