Design method for photovoltaic building integrated renovation in old residential areas

Abstract

The present invention discloses a design method for integrated photovoltaic building renovation in old residential areas, which specifically relates to the technical field of building renovation design, and comprises the following steps: firstly, a scanning device is used to obtain three-dimensional structural data of a building, and a suitable photovoltaic installation area is determined based on a preset renovation area level; then, the bearing capacity of photovoltaic components is calculated according to the renovation area, the total weight of photovoltaic components that can be supported is determined, and a preliminary distribution of components is designed; the inclination angle and azimuth of the photovoltaic components are optimized through sunshine analysis, and after installation, an inverter is used to convert direct current into alternating current and connected to a smart grid system, and finally, operation data is collected regularly, and a neural network is used to predict faults and evaluate health status; the present invention provides a feasible solution for large-scale promotion of renewable energy, and the installation of photovoltaic systems can make full use of idle space in old buildings, improve the utilization efficiency of solar energy resources, reduce dependence on traditional energy, and reduce energy consumption in old residential areas.

Description

Technical Field

[0001] The present invention relates to the technical field of building renovation design, and more specifically, to a design method for integrated renovation of photovoltaic buildings in old residential areas. Background Art

[0002] With the continuous growth of global energy demand and the increasingly severe environmental problems, photovoltaic power generation has been widely used as a renewable energy source. The complex building structure, low space utilization and high power demand of old urban communities have limited the large-scale application of photovoltaic systems. However, by accurately evaluating the building structure of old communities and combining advanced photovoltaic technology for building renovation, the power generation efficiency of photovoltaic systems can be effectively improved, the energy consumption of old communities can be reduced, and sustainable power supply can be provided to residents.

[0003] The existing photovoltaic system design methods mainly focus on the design of new buildings, and fail to fully consider the particularities of old buildings, such as load-bearing capacity, building aging, spatial layout, etc. At the same time, the operation and maintenance of photovoltaic systems are also difficult points of existing technologies, especially in old residential areas, where maintenance conditions are poor and system fault detection is not timely. Therefore, this paper proposes a design method for the integrated transformation of photovoltaic buildings in old residential areas. Summary of the invention

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] The design method for the integrated photovoltaic building renovation of old residential areas includes the following steps:

[0006] Use scanning equipment to obtain the three-dimensional structural data of the old community, and determine the renovation area of ​​the target house based on the preset house renovation area level;

[0007] According to the determined renovation area of ​​the target house, the load-bearing capacity of the photovoltaic modules is calculated to obtain the total weight of the photovoltaic modules that can be supported, and then the preliminary distribution design of the photovoltaic modules is carried out;

[0008] Conduct sunshine analysis on the target house, optimize the tilt angle and azimuth of the photovoltaic modules, obtain the optimized photovoltaic module distribution design plan and carry out transformation;

[0009] The inverter is used to convert the direct current of the photovoltaic module into alternating current, and the photovoltaic module is connected to the original power grid through the smart grid management system;

[0010] The operating data of PV modules is collected regularly and the health of PV modules is assessed using a neural network-based fault prediction algorithm.

[0011] In a preferred embodiment, the logic for determining the renovation area of ​​the target house is:

[0012] The three-dimensional structural data of the target house in the old community is obtained, and a house renovation area level form is preset. The renovation degree of each house renovation area is evaluated in turn to obtain a renovation degree evaluation value. When the renovation degree evaluation value reaches the preset renovation threshold for the first time, the house renovation area corresponding to the renovation degree evaluation value is marked as the renovation area of ​​the target house.

[0013] In a preferred embodiment, the logic for obtaining the transformation degree evaluation value is:

[0014] According to the preset house renovation area level table, the feasibility analysis of each level of renovation area is carried out in turn, and the yield strength of the building structure materials in the target renovation area is obtained and marked as , the effective area where photovoltaics can be installed is marked as , structural safety factor , the adjustment coefficient of building materials is set to take into account the difference in load-bearing capacity of different structural materials , the building aging coefficient is set to consider the impact of the building age on the load-bearing capacity , and then bring them into the calculation formula of the transformation degree evaluation value:

[0015] ; Indicates the transformation degree evaluation value of level i corresponding to the target transformation area.

[0016] Building aging factor It is obtained by calculating the ratio of the building's service life to the preset standard lifespan.

[0017] In a preferred embodiment, the tolerable total weight of the photovoltaic module refers to:

[0018] Get the renovation degree evaluation value corresponding to the renovation area of ​​the target house , as well as the foundation stability data, vibration impact coefficient, and climate impact coefficient of the target house are summarized as an input data combination, and then substituted into the pre-trained neural network model. The output result of the neural network model is the total weight of the photovoltaic components that can be tolerated.

[0019] In a preferred embodiment, the preliminary distribution design of photovoltaic modules includes the weight design, arrangement spacing design, maintenance space design, and electrical connection design between photovoltaic modules.

[0020] In a preferred embodiment, performing sunshine analysis on a target house and optimizing the tilt angle and azimuth angle of a photovoltaic module refers to:

[0021] Obtain the climate data and solar radiation data of the area where the target house is located and establish a solar radiation model, and then perform the following calculations:

[0022] ; represents the power generation of the PV module numbered i in the preliminary distribution design, represents the total annual sunshine amount of the solar radiation model in the renovation area of ​​the target house, represents the photovoltaic panel area of ​​the photovoltaic module numbered i in the preliminary distribution design, represents the photovoltaic panel power generation efficiency of the photovoltaic module numbered i in the preliminary distribution design, represents the tilt angle of the PV module numbered i in the preliminary distribution design, It represents the azimuth angle of the photovoltaic module numbered i in the preliminary distribution design, that is, the deflection angle relative to the south direction;

[0023] Then the power generation of all photovoltaic modules is summarized, with the goal of obtaining the maximum total power generation. At this time, the optimized tilt angle and azimuth angle corresponding to each photovoltaic module are obtained.

[0024] In a preferred embodiment, the photovoltaic assembly is connected to the original power grid through the smart grid management system, which means:

[0025] Smart meters and monitoring equipment are installed to collect real-time data on the building's electricity load. The smart grid management system adjusts the use and distribution of photovoltaic power generation according to preset strategies based on the monitored load data.

[0026] In a preferred embodiment, using a neural network-based fault prediction algorithm to perform health assessment on a photovoltaic module refers to:

[0027] Obtain the collected operating data of the photovoltaic modules and pre-process the collected raw data to remove noise and abnormal data, and standardize the data so that it can be input into the neural network model for analysis;

[0028] Constructing a neural network model for predicting the health status of a photovoltaic module, wherein the input layer includes a plurality of preset characteristic parameters of the photovoltaic module;

[0029] The hidden layer of the neural network extracts features from the input data through a nonlinear activation function to identify potential failure modes in the operating status of the photovoltaic system;

[0030] The output layer generates fault prediction results, including the health assessment value of the PV module and the probability of fault occurrence. The sum of the health assessment value and the probability of fault occurrence is one.

[0031] Technical effects and advantages of the present invention:

[0032] Through detailed 3D scanning of the building structures of old residential areas and evaluation of the renovation areas, we ensured that the installation area of ​​PV modules was safe and reliable, avoiding the problem of structural overload. The installation of PV systems can make full use of the idle space of old buildings, such as roofs, south-facing walls and balconies, improve the utilization efficiency of solar energy resources, reduce dependence on traditional energy, and reduce energy consumption in old residential areas.

[0033] The design of photovoltaic system components has been optimized in combination with the building's load-bearing capacity and sunshine conditions. By optimizing the tilt angle and azimuth of photovoltaic components, the components can be ensured to absorb solar energy to the maximum extent, thus improving the power generation efficiency of the photovoltaic system. Through scientific design and layout within a limited space, the maximum power generation is achieved.

[0034] Through the introduction of smart grid management systems, photovoltaic power generation not only meets the building's own electricity needs, but can also feed back to the grid when there is excess power generation, achieving efficient power management and maximizing benefits. When the photovoltaic system generates insufficient power, it can automatically switch to the grid to ensure continuous power supply for the building. This grid-connected operation mode not only improves the utilization rate of the photovoltaic system, but also reduces power waste.

[0035] By introducing the fault prediction and health assessment function based on neural networks, the operating status of photovoltaic modules can be monitored in real time. The neural network can identify potential faults of the photovoltaic system through learning historical data and issue early warnings, avoiding the occurrence of serious faults and reducing losses caused by system downtime or maintenance. This predictive maintenance model greatly reduces the long-term maintenance cost of the system.

[0036] The present invention can flexibly adapt to the building structure and use environment of different old communities. By evaluating the degree of renovation of different areas one by one, it ensures that the renovation plan is gradually promoted on the basis of safety and economy. It avoids the construction complexity and safety risks brought by traditional large-scale renovation, and the renovation process is more efficient and controllable.

[0037] The photovoltaic building integrated transformation method of the present invention not only contributes to the energy-saving transformation of old residential areas, but also provides a feasible solution for the large-scale promotion of renewable energy. It can help residents of old residential areas reduce electricity expenses and improve their quality of life, while promoting the application and development of urban green energy, with significant social and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0039] Figure 1 This is a schematic diagram of the design method for integrated photovoltaic building renovation in old residential areas in the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Reference Figure 1 The following embodiments are obtained:

[0042] Embodiment 1: A design method for photovoltaic building integration renovation in an old residential area, comprising the following steps:

[0043] Use scanning equipment to obtain the three-dimensional structural data of old communities, and determine the renovation area of ​​the target house based on the preset house renovation area level; use laser scanning (LiDAR) or drone-mounted photography equipment to obtain the three-dimensional structural data of houses in old communities. These data include detailed information such as the height, roof shape, wall structure, and building materials of the house. By analyzing these data and combining them with the preset house renovation area level form, the renovation potential and limitations of each area are evaluated in turn. Then, based on conditions such as the bearing capacity of the building structure and available space, determine which areas are suitable for photovoltaic renovation. This step can comprehensively analyze the house structure by accurately obtaining the three-dimensional data of the house, ensuring that the selection of the renovation area is based on actual conditions. It helps determine the area suitable for installing photovoltaic modules, ensuring that the subsequent installation process is safe and reliable, while avoiding potential safety hazards in areas that are not suitable for installation.

[0044] According to the determined renovation area of ​​the target house, the load-bearing capacity of the photovoltaic modules is calculated to obtain the total weight of the photovoltaic modules that can be supported, and then the preliminary distribution design of the photovoltaic modules is carried out; after the renovation area of ​​the target house is determined, a detailed load-bearing analysis is carried out. The calculation steps include evaluating the load-bearing capacity of building materials (such as the load-bearing limit of the roof or wall), the degree of building aging and other parameters, and calculating the maximum weight that the photovoltaic modules can bear in different areas. According to the load-bearing capacity, the preliminary distribution design is carried out to ensure that the photovoltaic modules are evenly distributed in these areas and do not exceed the safe load-bearing range of the building structure. This step ensures that the photovoltaic modules are installed within the load-bearing range of the building to avoid damage to the building structure due to overload. At the same time, the reasonable distribution of photovoltaic modules helps to improve space utilization and reduce the risk of system failure caused by uneven distribution.

[0045] Conduct sunshine analysis on the target house, optimize the tilt angle and azimuth of the photovoltaic modules, obtain the optimized photovoltaic module distribution design plan and carry out the transformation; use the sunshine analysis tool, based on the geographical location of the house, the orientation of the building, the slope of the roof and wall, and other information, refer to the sunshine conditions in each area, analyze the path of solar radiation throughout the year, and optimize the installation angle (tilt angle) and azimuth (direction) of the photovoltaic modules to maximize the absorption of solar energy. Perform the final module installation and transformation according to the optimized design plan. By optimizing the tilt angle and azimuth of the photovoltaic modules, the absorption of solar radiation can be maximized and the power generation efficiency of the system can be improved. A reasonable design plan can also reduce the system operating cost and extend the service life of the photovoltaic modules.

[0046] The inverter is used to convert the direct current of the photovoltaic module into alternating current. The photovoltaic module is connected to the original power grid through the smart grid management system. The direct current generated by the photovoltaic module needs to be converted into alternating current (AC) through the inverter to meet the standard parameters of the power grid (such as voltage, frequency, etc.). The converted electric energy is incorporated into the original power grid of the building through the smart grid management system. The smart grid system can monitor the matching degree of photovoltaic power generation with the building's power demand in real time, and automatically adjust the use, storage or feedback of photovoltaic power to the power grid. This step ensures seamless connection between the photovoltaic system and the building's power system, so that photovoltaic power generation can not only meet self-use, but also feedback excess power to the power grid to increase economic benefits. Through the smart grid management system, energy efficiency can be improved and power distribution can be optimized.

[0047] Regularly collect the operating data of PV modules and use the neural network-based fault prediction algorithm to evaluate the health of PV modules. Install a sensor system to regularly collect the operating data of PV modules, such as voltage, current, temperature, and power generation efficiency. Input this data into the neural network-based fault prediction model for analysis and evaluation. The neural network model identifies the potential failure modes of the PV system by learning from historical data, and warns of possible component damage or system failure in advance. Through regular data collection and health assessment, faults in the PV system can be discovered in advance before they occur, reducing maintenance costs and avoiding system downtime and performance degradation. The application of neural networks can accurately predict the health status of components, help achieve more effective preventive maintenance, and extend the service life of the system.

[0048] Each step is interconnected to form a complete photovoltaic system design, installation, operation and maintenance process. From the initial 3D scanning and area determination, to the optimized design of photovoltaic modules, load calculation and grid-connected operation, to data-based intelligent fault prediction, each step ensures the safety, efficiency and long-term reliability of the photovoltaic system. The implementation of these steps not only helps to improve the efficiency of photovoltaic power generation, but also reduces the risk of failure of the system, namely photovoltaic modules, through intelligent technology, and ensures the sustainable operation of the photovoltaic building integrated renovation design method for old communities.

[0049] The logic for determining the renovation area of ​​the target house is as follows: obtain the three-dimensional structural data of the target house in the old community, preset a house renovation area level form, evaluate the renovation degree of each house renovation area in turn, and obtain the renovation degree evaluation value. When the renovation degree evaluation value reaches the preset renovation threshold for the first time, mark the house renovation area corresponding to the renovation degree evaluation value as the renovation area of ​​the target house. By obtaining the three-dimensional structural data of the target house and evaluating each area one by one according to the preset renovation area level form, it can ensure that the selection of the renovation area is based on actual conditions. In this way, areas that are not suitable for installing photovoltaic modules (such as areas with too old structures or insufficient bearing capacity) can be excluded, thereby improving the safety and efficiency of the renovation.

[0050] By evaluating the degree of transformation of each transformation area in turn and calculating the transformation degree evaluation value, it is ensured that each area has been analyzed in detail. The transformation degree evaluation value reflects the workload or carrying capacity that the area can bear during the transformation process. When the transformation degree evaluation value reaches the preset transformation threshold for the first time, it means that the area has sufficient conditions for photovoltaic transformation, so as to reasonably determine the optimal transformation area. The transformation degree evaluation value combined with the preset threshold can effectively reduce the potential risks in the transformation process. Without a systematic evaluation, direct transformation may cause the area to be overloaded or unsuitable for transformation, causing structural problems or safety hazards. Through this logical process, it can ensure that the selection of transformation areas is more secure and reduce the probability of structural overload or other accidents.

[0051] Through this evaluation logic, the selection of renovation areas is more accurate, thereby effectively utilizing building resources and funds. Renovation in unsuitable areas may lead to waste of resources and reduced return on investment, but by evaluating and selecting suitable areas, the benefits of investment can be maximized. This logic ensures the efficiency of the entire renovation process. By selecting renovation areas in a structured and step-by-step manner, blind design and construction can be avoided, making the design and installation of the entire photovoltaic system more planned. The evaluation and selection of each renovation area is standardized and automated, which can reduce errors in human judgment and improve renovation efficiency.

[0052] The preset house renovation area level form refers to classifying different areas of the target house and grading them according to the priority or feasibility of the renovation. According to the characteristics and structure of these areas, determine whether each area is suitable for the installation of photovoltaic modules or other renovation work. Through this classification form, each part of the house can be systematically evaluated and planned for renovation. An example is as follows: Roof, level: 1 (priority); Renovation degree assessment index: 95 (high); Reasons for suitable photovoltaic installation: The roof is exposed to the sun and has a large area; it can be directly exposed to sunlight and is conducive to the installation of photovoltaic modules; Restrictions: The load-bearing capacity is limited, and it is necessary to detect whether it is suitable for large-scale module installation;

[0053] Wall (south facing), level: 2 (medium); Renovation degree assessment index: 85 (medium); Reasons for being suitable for photovoltaic installation: South-facing walls have sufficient sunlight and are suitable for installing vertical photovoltaic modules or glass photovoltaic curtain walls; Restrictions: The wall structure must be stable and not affect the appearance of the building;

[0054] Balcony, level: 3 (suboptimal); Renovation evaluation index: 70 (medium); Reasons for being suitable for installing photovoltaics: Balcony space is usually relatively open, and small photovoltaic modules can be installed or combined with sunshade functions; Restrictions: Balcony space is small and needs to be used by residents.

[0055] Through the preset house renovation area level form, the feasibility of each area can be systematically evaluated to ensure that the renovation of each area is based on reasonable analysis. Different areas are assigned different levels to help decide which areas to renovate first, which areas can be postponed or require special planning. Areas with high renovation priority can make full use of sunlight resources and maximize power generation benefits, while areas with low priority can be handled flexibly to avoid unnecessary waste of resources.

[0056] Buildings in old communities often have insufficient load-bearing capacity. Renovation of a single area can ensure the safety of the renovation. If multiple areas are renovated at the same time, the structural load of the building may increase, exceeding its load-bearing limit, thereby increasing safety risks. For example, the roof may have been under the pressure of aging or damage. In this case, if photovoltaic panels are installed on the walls or balconies at the same time, the building may exceed the safe load. A single renovation area can make structural assessment and renovation easier to control.

[0057] Cost is an important consideration when making a renovation. Individual area renovations can be done gradually based on the most profitable areas, thus achieving a faster return on investment. Renovating multiple areas at the same time may result in increased initial costs and a lower long-term return on investment. For example, the roof is usually the best area for renovation because it receives the most sunlight and has a higher return on the cost of the renovation investment. Walls and balconies may require higher installation and maintenance costs and have relatively low power generation efficiency. Therefore, when funds are limited, it is more advantageous to prioritize the renovation of the most profitable single area.

[0058] The sunshine conditions in different areas are different. The transformation of a single area can optimize the lighting design for a specific area to ensure that the photovoltaic modules can maximize the power generation efficiency in that area. Multi-area transformation may face differences in sunshine conditions in various areas, making it difficult to optimize the power generation efficiency of all areas at the same time. The roof is usually the area with the most sunshine, while the walls or balconies may be blocked by surrounding buildings or objects during certain periods, affecting the sunshine. Therefore, through the transformation of a single area, you can focus on optimizing the power generation efficiency of the optimal area and avoid installing photovoltaic modules in areas with poor sunshine conditions, which will lead to reduced efficiency.

[0059] The installation and maintenance of a single area renovation is relatively simple and requires low technical requirements. If multiple areas are renovated at the same time, the construction difficulty will be greatly increased, and different types of equipment and technical support may be required. At the same time, maintenance and fault detection of multiple areas will become more complicated. PV systems on roofs can usually be installed and maintained in a relatively standardized manner, while installations on walls or balconies may require special brackets and structural adjustments, and maintenance of these areas requires more manpower and time costs. If there are too many areas to be renovated, the maintenance process will become complicated and difficult to manage.

[0060] Large-scale renovation of multiple areas in old residential areas may affect the appearance of the building. Renovation of individual areas can reduce interference with the building's appearance, maintain the building's aesthetics, and is easier to obtain approval from owners or residents. If multiple areas are renovated at the same time, it may cause residents' disgust or planning restrictions. For example, photovoltaic panels on walls and balconies may affect the appearance of the building's facade, especially in historical buildings or areas with strict urban planning restrictions. Therefore, giving priority to rooftops or other inconspicuous areas for renovation can reduce conflicts with urban management or residents.

[0061] The logic for obtaining the renovation degree assessment value is as follows: According to the preset house renovation area grade form, the feasibility analysis of each grade of renovation area is carried out in turn, and the yield strength of the building structure material in the target renovation area is obtained and marked as The yield strength of building materials is a parameter that is preset based on material properties and is usually determined by building codes and standards. The effective area where photovoltaics can be installed is marked as , structural safety factor The structural safety factor is also preset, which usually takes into account the design standards of the building and the expected safety factor to ensure the safety of the building in long-term use. The adjustment factor of building materials is set to take into account the difference in the bearing capacity of different structural materials. , the building material adjustment factor is preset according to the specific materials used in the building. Different materials have different load-bearing capacities, so the adjustment factor is also predetermined. The building aging factor is set to consider the impact of the building age on the load-bearing capacity. , and then substitute them into the calculation formula of the transformation degree evaluation value:

[0062] ; Indicates the renovation degree assessment value of level i corresponding to the target renovation area. The renovation degree assessment value is calculated based on comprehensive parameters such as the strength of the building structure, the effective area where PV modules can be installed, and the bearing capacity of building materials. This value reflects whether a certain area is suitable for PV renovation. The larger the renovation degree assessment value, the more suitable the area is for renovation, with better bearing capacity and safety, and can often withstand more PV module installations.

[0063] Building aging factor It is obtained by calculating the ratio of the building's service life to the preset standard life. The building aging coefficient is preset based on the ratio of the building's service life to the standard life. As the building's service life increases, the aging coefficient will decrease, indicating a decrease in the building's bearing capacity.

[0064] The total weight of the photovoltaic modules that can be tolerated refers to: Obtain the renovation degree assessment value corresponding to the renovation area of ​​the target house , as well as the foundation stability data, vibration impact coefficient, and climate impact coefficient of the target house are summarized as an input data combination, and then substituted into the pre-trained neural network model. The output result of the neural network model is the total weight of the photovoltaic components that can be tolerated.

[0065] From the previous transformation area grade form and assessment results, obtain the transformation degree assessment value of the target area. This is a value obtained by analyzing factors such as the bearing capacity, aging coefficient, and building materials of the building structure, indicating whether the area is suitable for photovoltaic transformation. Foundation stability directly affects the bearing capacity of the building. Through soil testing, geological surveys, building settlement monitoring, etc., obtain the bearing capacity, settlement of the house foundation, and whether there are foundation subsidence and other problems. These data can be obtained from professional geological companies or through exploration reports during the construction of the house. The vibration impact coefficient reflects the vibration impact of the building's environment, such as whether there are railways, subways, traffic arteries, or industrial equipment near the building. Large vibrations may affect the structural stability and bearing capacity of the building. This coefficient can be measured by environmental monitoring equipment (such as vibration sensors) or obtained from regional traffic planning and geological assessments. The climate impact coefficient takes into account the impact of local extreme weather conditions (such as strong winds, blizzards, and freezing) on ​​buildings and photovoltaic systems. It is usually obtained through historical data from the meteorological department (such as wind force, snow load, temperature changes, etc.). For areas prone to snowstorms, the climate impact factor is higher because these factors put additional pressure on the load-bearing capacity of buildings.

[0066] The renovation degree assessment value, foundation stability data, vibration impact coefficient, and climate impact coefficient are summarized into an input data group. Each parameter represents a dimension that affects the installation of the photovoltaic system. Through the comprehensive input of these data, the bearing capacity and safety of the house structure can be evaluated. The above summary data is input into a pre-trained neural network model. The model has been trained with historical building data and can identify the relationship between the load-bearing capacity of different buildings and the installation of photovoltaic systems. The neural network model will output the total weight of photovoltaic modules that the target house can bear in the renovation area based on the input structural data and external influencing factors. This result will be used to optimize the distribution design and installation planning of photovoltaic modules.

[0067] The preliminary distribution design of PV modules includes the weight design, arrangement spacing design, maintenance space design, and electrical connection design between PV modules, and is usually performed by professionals.

[0068] Weight design of PV modules: Determine the weight of each PV module to ensure that it does not exceed the load-bearing capacity of the building structure in the installation area. At the same time, the overall power generation demand is met by selecting PV panels of different sizes. Weight of PV modules and PV panel area: The weight of each PV module is proportional to the area of ​​the PV panel. The larger the area, the heavier the weight. Therefore, it is necessary to design PV modules of different sizes and weights based on the total weight of PV modules that can be carried and the load-bearing capacity of each area. Regional weight distribution: The load-bearing capacity of each installation area is different. The weight of PV modules suitable for installation needs to be determined based on the transformation assessment value of the area. Larger areas may be able to carry heavier modules, while smaller or less load-bearing areas are suitable for lighter modules. Material selection: The bracket and installation material of the PV module also affect the weight. For example, the use of lightweight aluminum alloy brackets can reduce the total weight of the PV module, thereby improving safety.

[0069] Arrangement spacing design of PV modules: If the arrangement spacing of PV modules is too small, the modules in the front row may block the modules in the back row, especially in the morning and evening when the sun angle is low. It is necessary to ensure that each PV module can receive sufficient sunlight. Spacing formula: The arrangement spacing design can be calculated through geometric relationships, usually considering the sun angle, module height and distance. A common formula is: d≥h×tan(k), where k is the minimum sun angle of the PV module installation area, h is the height of the module, and d is the minimum spacing between modules. Usually, 1.2-1.5 times d is used as the minimum spacing between modules in the design. This is to provide sufficient adjustment and optimization space for the subsequent optimization of the PV module angle.

[0070] Reserve enough space between and around PV panels for technicians to perform maintenance work. The width of the passage is usually required to be at least 0.6 meters to 1.2 meters, and the specific width depends on the local installation specifications and the characteristics of the building structure. Safe passage: The design of the passage needs to take into account the layout of the components and the safe distance from the edge of the roof, especially when installing on multi-story buildings, to ensure that protective measures are taken to prevent people from slipping. Cleaning and maintenance requirements: PV panels need to be cleaned regularly to maintain a high power generation efficiency, so the maintenance space should ensure that the cleaning equipment can work properly. The equipment space usually requires sufficient height and width to facilitate the operation of the cleaning device.

[0071] Electrical connection design between photovoltaic modules: Series and parallel design: Photovoltaic modules are usually connected in series and parallel. Series connection can increase voltage, while parallel connection can increase current. When designing, it is necessary to reasonably plan the series and parallel relationship of the modules according to the input requirements of the inverter to ensure that the voltage and current of the system meet the standards. Wiring design: The wiring of the cable needs to consider the shortest path to reduce cable loss. The wiring should usually avoid complex interlacing to ensure the safety and stability of the cable, while preventing mechanical damage and environmental erosion. Inverter configuration: The inverter is a key device in the photovoltaic system that converts direct current into alternating current. The electrical connection design between the modules needs to ensure that each module can be properly connected to the inverter. The capacity selection of the inverter needs to match the power generation capacity of the module to avoid power loss caused by overload or too small capacity. Grounding design: In order to ensure the safety of the system, the electrical connection between the photovoltaic module and the inverter needs to be equipped with a complete grounding system to prevent electrical accidents caused by lightning strikes or current leakage.

[0072] Analyzing the sunshine of the target house and optimizing the tilt angle and azimuth of the photovoltaic modules refer to:

[0073] Obtain the climate data and solar radiation data of the target house area and establish a solar radiation model, and then optimize the tilt angle and azimuth of the photovoltaic module: Through sunshine analysis, determine the tilt angle and azimuth of the photovoltaic module in order to maximize the power generation efficiency of the photovoltaic module. Different tilt angles and azimuths will directly affect the angle at which the photovoltaic panel receives solar radiation, thereby affecting the power generation of the module. This formula combines the local solar radiation data and the geographical location of the building to ensure that each photovoltaic module can receive sunlight at the optimal angle, thereby increasing power generation.

[0074] Use of solar radiation model: The solar radiation model can provide local sunshine information, combined with the tilt angle and azimuth of the photovoltaic module, and calculate the power generation of each photovoltaic module through a formula. After the power generation of multiple photovoltaic modules is aggregated, the total power generation can be obtained, thereby optimizing the module layout.

[0075] Sources of climate data: Climate data can be obtained through local meteorological bureaus or meteorological websites, such as the National Meteorological Bureau, NASA climate data resources, etc. Data include: sunshine duration, daily average temperature, wind speed, precipitation, etc.; special meteorological data tools or software, such as Meteonorm, PVGIS, etc., can be used to obtain detailed climate and solar radiation data.

[0076] Sources of solar radiation data: Solar radiation data can be obtained through online tools or databases, such as: SolarGIS: Provides high-precision solar radiation data, especially suitable for the design and optimization of photovoltaic systems. PVGIS: This is a free online platform that can provide solar radiation and photovoltaic power generation potential data around the world. Data content: Daily or hourly global horizontal radiation, direct normal radiation, diffuse radiation, etc.

[0077] Model Types: Solar radiation models usually include three basic radiation types: Global Horizontal Radiation (GHI): The total amount of solar radiation reaching the ground level, including direct and diffuse radiation. Direct Normal Radiation (DNI): The direct solar radiation incident vertically on the solar panel. Diffuse Radiation: The solar radiation scattered by the atmosphere.

[0078] Model building steps: Data input: Input solar radiation, temperature and other climate data from the weather station or online tools. Regional division: Set the basic parameters of the model based on the building's geographical location, latitude, and building orientation. Adjustment of tilt angle and azimuth: Use optimization algorithms to establish different solar radiation incidence models for different tilt angles and azimuths. Using optimization algorithms means: ; represents the power generation of the PV module numbered i in the preliminary distribution design, represents the total annual sunshine amount of the solar radiation model in the renovation area of ​​the target house, represents the photovoltaic panel area of ​​the photovoltaic module numbered i in the preliminary distribution design, represents the photovoltaic panel power generation efficiency of the photovoltaic module numbered i in the preliminary distribution design, represents the tilt angle of the PV module numbered i in the preliminary distribution design, It represents the azimuth of the PV module numbered i in the preliminary distribution design, that is, the deflection angle relative to the south direction; then the power generation of all PV modules is summarized, with the goal of obtaining the maximum total power generation. At this time, the optimized tilt angle and azimuth corresponding to each PV module are obtained.

[0079] According to the requirements of the present invention and the layout of the photovoltaic modules, the most appropriate radiation type selection depends on the actual lighting environment and the way the modules are installed. Generally speaking, the global horizontal radiation (GHI) is the most appropriate choice. The global horizontal radiation refers to the total amount of solar radiation reaching the horizontal plane of the ground, including direct radiation and diffuse radiation. Application scenario: GHI is the most commonly used radiation indicator, especially suitable for evaluating photovoltaic systems installed at fixed angles or horizontally. It can accurately reflect the total solar energy reaching the photovoltaic modules, whether it is direct sunlight or indirect light caused by atmospheric scattering.

[0080] Reasons for suitability for the present invention: The photovoltaic modules of the present invention are installed on fixed areas such as the roof or wall of a building. There may be no equipment for automatic sun tracking, and the inclination angle and azimuth angle are relatively fixed. Using GHI as a radiation model can fully consider the combined impact of direct radiation and scattered radiation on photovoltaic modules, providing a more accurate power generation assessment. For vertically or tilted photovoltaic modules, scattered radiation can significantly increase power generation on cloudy days or under undesirable lighting conditions. GHI includes both scattered and direct radiation, so it can better assess the true power generation potential.

[0081] Direct normal radiation is mainly used in tracking photovoltaic systems, which can adjust the orientation of photovoltaic modules and track the position of the sun in real time, so that the modules always face the sun and maximize the absorption of direct radiation. Reasons for being unsuitable for the present invention: The photovoltaic modules in the present invention are fixedly installed on the building and cannot track the sun. Therefore, although direct normal radiation provides the strongest solar radiation, due to the fixed nature of the installation, all direct radiation cannot be fully utilized. In cloudy or shadowed conditions, direct radiation is reduced, and at this time GHI can better reflect the actual power generation of the module because GHI includes scattered radiation, while direct normal radiation only includes direct radiation.

[0082] Diffuse radiation (DHI) is radiation scattered by clouds and particulate matter in the atmosphere, not light directly from the sun. Application scenarios: DHI is mainly applicable to high-latitude areas or frequently cloudy areas, where direct sunlight is limited and diffuse radiation accounts for a large part of the total radiation. Reasons for not being fully suitable for the present invention: Although diffuse radiation provides power generation capabilities for photovoltaic systems on cloudy days, using DHI alone will ignore the contribution of direct radiation. Most of the electricity generated by photovoltaic modules still comes from direct radiation, especially on sunny days, so if only DHI is used as a model, it is impossible to accurately evaluate the power generation on sunny days.

[0083] The photovoltaic modules are connected to the original power grid through the smart grid management system, which means: installing smart meters and monitoring equipment, collecting the power load data of the building in real time, and the smart grid management system adjusts the use and distribution of photovoltaic power generation according to the preset strategy based on the monitored load data. Specifically: when the load demand is low, the smart grid gives priority to the use of electricity generated by photovoltaic modules to reduce the power consumption obtained from the power grid. When the photovoltaic power generation power is higher than the power load, the power of the photovoltaic modules is first supplied to the internal use of the building;

[0084] Surplus power is fed back to the grid: When the power generated by the photovoltaic modules is greater than the power load of the building, the power is fed back to the grid through the smart grid management system and sold back to the grid according to the agreement set by the power company. Steps: When the photovoltaic power generation in the building is greater than the power demand, the smart grid management system detects the surplus power; the system feeds the excess power back to the grid through the grid-connected device; the smart grid management system monitors the feedback power synchronously with the grid and settles with the power company.

[0085] Grid power supply switching: When the photovoltaic power generation is insufficient (for example, at night or on cloudy days), the building will first obtain power from the original grid. The smart grid management system automatically switches to grid power supply by monitoring the photovoltaic power generation status in real time. Steps: When the smart grid management system detects that the power generation of the photovoltaic module is lower than the load demand, it automatically switches to obtaining power from the grid; during the power switching process, the smart grid management system maintains automatic adjustment of voltage and frequency to ensure power supply continuity.

[0086] Collaborative work of energy storage equipment: If the system includes energy storage equipment (such as battery packs), the smart grid management system can dispatch the energy storage equipment according to the real-time power situation, and use the energy storage power first when the photovoltaic power generation is insufficient. Steps: The smart grid management system monitors the charging status of photovoltaic power generation and energy storage batteries; when photovoltaic power generation is insufficient, the power in the energy storage equipment is used first; when the power of the energy storage equipment is exhausted, the system automatically switches to the grid power supply.

[0087] The photovoltaic system and the smart grid management system are equipped with self-checking and fault detection functions, which can regularly check the operating status of the equipment, and promptly alarm and cut off the power supply when a fault is found. Steps: The smart grid management system regularly performs self-checks on the photovoltaic components; when a fault or abnormality is detected, the system automatically cuts off the power supply of the faulty part and issues an alarm. Using a neural network-based fault prediction algorithm to conduct health assessment on photovoltaic components means: obtaining the collected operating data of the photovoltaic components and preprocessing the collected raw data, removing noise and abnormal data, and standardizing the data so that it can be input into the neural network model for analysis, ensuring that the data input into the neural network is consistent and high-quality, and avoiding noise interference with the prediction accuracy of the model; constructing a neural network model for predicting the health status of photovoltaic components, the input layer includes multiple preset characteristic parameters of the photovoltaic components, including but not limited to the key parameters such as the voltage, current, temperature, and power generation efficiency of the components; the hidden layer of the neural network extracts features from the input data through a nonlinear activation function to identify potential fault modes in the operating status of the photovoltaic system; the output layer generates fault prediction results, and the output includes the health assessment value of the photovoltaic components and the probability of fault occurrence, and the sum of the health assessment value and the probability of fault occurrence is one. The neural network is established based on historical operating data and deployed after performance verification. When the health assessment value is lower than the set health threshold, the system automatically cuts off the power supply to the faulty part and issues an alarm.

[0088] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0089] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0090] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0091] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0092] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The design method for photovoltaic building integration renovation in old residential areas is characterized by: The following steps are involved: Use scanning equipment to obtain the three-dimensional structural data of the old community, and determine the renovation area of ​​the target house based on the preset house renovation area level; According to the determined renovation area of ​​the target house, the load-bearing capacity of the photovoltaic modules is calculated to obtain the total weight of the photovoltaic modules that can be supported, and then the preliminary distribution design of the photovoltaic modules is carried out; Conduct sunshine analysis on the target house, optimize the tilt angle and azimuth of the photovoltaic modules, obtain the optimized photovoltaic module distribution design plan and carry out transformation; The inverter is used to convert the direct current of the photovoltaic module into alternating current, and the photovoltaic module is connected to the original power grid through the smart grid management system; Regularly collect the operating data of PV modules and use the neural network-based fault prediction algorithm to conduct health assessment of PV modules; The logic for determining the renovation area of ​​the target house is: The three-dimensional structural data of the target house in the old residential area is obtained, and a house renovation area level form is preset. The renovation degree of each house renovation area is evaluated in turn to obtain a renovation degree evaluation value. When the renovation degree evaluation value reaches the preset renovation threshold for the first time, the house renovation area corresponding to the renovation degree evaluation value is marked as the renovation area of ​​the target house; The logic for obtaining the transformation evaluation value is: According to the preset house renovation area level table, the feasibility analysis of each level of renovation area is carried out in turn, and the yield strength of the building structure materials in the target renovation area is obtained and marked as , the effective area where photovoltaics can be installed is marked as , structural safety factor , the adjustment factor of building materials is set to take into account the difference in load-bearing capacity of different structural materials , the building aging coefficient is set to consider the impact of the building age on the load-bearing capacity , and then bring them into the calculation formula of the transformation degree evaluation value: ; represents the transformation degree evaluation value of level i corresponding to the target transformation area; Analyzing the sunshine of the target house and optimizing the tilt angle and azimuth of the photovoltaic modules refer to: Obtain the climate data and solar radiation data of the area where the target house is located and establish a solar radiation model, and then perform the following calculations: ; represents the power generation of the PV module numbered i in the preliminary distribution design, represents the total annual sunshine amount of the solar radiation model in the renovation area of ​​the target house, represents the photovoltaic panel area of ​​the photovoltaic module numbered i in the preliminary distribution design, represents the photovoltaic panel power generation efficiency of the photovoltaic module numbered i in the preliminary distribution design, represents the tilt angle of the PV module numbered i in the preliminary distribution design, It represents the azimuth angle of the photovoltaic module numbered i in the preliminary distribution design, that is, the deflection angle relative to the south direction; Then the power generation of all photovoltaic modules is summarized, with the goal of obtaining the maximum total power generation. At this time, the optimized tilt angle and azimuth angle corresponding to each photovoltaic module are obtained.

2. The photovoltaic building integrated reconstruction design method for old residential areas according to claim 1 is characterized in that: Building aging factor It is obtained by calculating the ratio of the building's service life to the preset standard lifespan.

3. The photovoltaic building integrated reconstruction design method for old residential areas according to claim 2 is characterized in that: The total weight of photovoltaic modules that can be supported is: Get the renovation degree evaluation value corresponding to the renovation area of ​​the target house , as well as the foundation stability data, vibration impact coefficient, and climate impact coefficient of the target house are summarized as an input data combination, and then substituted into the pre-trained neural network model. The output result of the neural network model is the total weight of the photovoltaic components that can be tolerated.

4. The photovoltaic building integrated renovation design method for old residential areas according to claim 3 is characterized in that: The preliminary distribution design of PV modules includes the weight design, arrangement spacing design, maintenance space design, and electrical connection design between PV modules.

5. The photovoltaic building integrated reconstruction design method for old residential areas according to claim 4 is characterized in that: The photovoltaic modules are connected to the original power grid through the smart grid management system, which means: Smart meters and monitoring equipment are installed to collect real-time data on the building's electricity load. The smart grid management system adjusts the use and distribution of photovoltaic power generation according to preset strategies based on the monitored load data.

6. The photovoltaic building integrated reconstruction design method for old residential areas according to claim 5 is characterized in that: Using neural network-based fault prediction algorithms to perform health assessments on PV panels refers to: Obtain the collected operating data of the photovoltaic modules and pre-process the collected raw data to remove noise and abnormal data, and standardize the data so that it can be input into the neural network model for analysis; Constructing a neural network model for predicting the health status of a photovoltaic module, wherein the input layer includes a plurality of preset characteristic parameters of the photovoltaic module; The hidden layer of the neural network extracts features from the input data through a nonlinear activation function to identify potential failure modes in the operating status of the photovoltaic system; The output layer generates fault prediction results, including the health assessment value of the PV module and the probability of fault occurrence. The sum of the health assessment value and the probability of fault occurrence is one.

Images