A dynamic photovoltaic control method adapted to regional climate

Abstract

The application discloses a dynamic photovoltaic control method suitable for regional climate, relates to the technical field of photovoltaic control, and establishes a grading determination mechanism of normal seasonal working conditions and severe weather working conditions through climate working condition calibration; three photovoltaic skin forms of a summer sun-shading and heat-dissipating type, a winter light-transmitting and heat-generating type and a transition season balanced adaptation type are constructed under normal working conditions, so that the synergy optimization of photovoltaic power generation and building energy saving is realized; under severe weather, the skin structure is actively changed through folding, closing and locking and reinforcing, so that the photovoltaic material is comprehensively protected; a standardized carbon emission simulation model covering the whole stage of building material production, construction, operation and demolition is constructed, and a calculation system is established; finally, a multi-objective closed-loop optimization framework with minimization of whole life cycle carbon emission as an objective function and multiple performance indexes as constraint conditions is formed, the global optimal control parameter is solved through a genetic algorithm, and a feedback updating regulation strategy is realized, so that the organic unification of climate adaptation, extreme protection and low-carbon optimization is realized.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic control technology, specifically a dynamic photovoltaic control method adapted to regional climate. Background Technology

[0002] Building-integrated photovoltaics (BIPV) technology deeply integrates photovoltaic (PV) components with the building envelope, enabling the building facade to serve multiple functions including power generation, enclosure, and decoration. It achieves on-site production and consumption of clean electricity without requiring additional land resources, becoming a core technological path for zero-carbon and near-zero-energy buildings. With the increasing frequency of extreme weather events and continuously improving building energy efficiency standards, single fixed PV facades are no longer sufficient to meet the synergistic optimization needs of building thermal performance and PV power generation efficiency in different climate zones and seasons. Dynamically adjustable PV building facades, with their adaptable form and operating conditions, are gradually becoming a research hotspot in the industry.

[0003] Existing building-integrated photovoltaic (BIPV) systems generally suffer from several technical defects: First, they lack seasonal adaptability, making it difficult to coordinate power generation and energy conservation: the tilt angle and arrangement of fixed photovoltaic modules are fixed once installed, making it impossible to increase the shading area in summer to reduce the building's cooling load, and impossible to optimize the angle of sunlight in winter to improve power generation efficiency and passive heat gain; Second, they have weak protection against extreme weather: exposed photovoltaic modules are directly subjected to the impact of strong winds and long-term erosion by rainwater in the face of severe weather conditions such as typhoons and rainstorms, which can easily lead to structural deformation, sealing failure, water ingress into junction boxes, and other malfunctions; Third, a full life-cycle low-carbon evaluation system is lacking: most schemes only use the power generation during the operation phase as a single evaluation indicator, failing to cover the full-stage accounting of carbon emissions from building materials, construction, operation and maintenance, and demolition and recycling, and the accounting boundaries and dimensional standards of different studies are inconsistent, making it difficult to truly reflect the comprehensive carbon reduction benefits of photovoltaic systems. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a dynamic photovoltaic control method adapted to regional climate. This method establishes a graded judgment mechanism for normal seasonal operating conditions and severe weather conditions through climate condition calibration. Under normal operating conditions, it constructs three photovoltaic skin types: summer shading and heat dissipation type, winter light-transmitting and heat-generating type, and transitional season balanced adaptation type, achieving synergistic optimization of photovoltaic power generation and building energy conservation. Under severe weather conditions, it actively changes the skin structure through closure and locking reinforcement to achieve comprehensive protection of photovoltaic materials. A standardized carbon emission simulation model covering the entire stages of building material production, construction, operation, and demolition is constructed to establish a calculation system. Finally, a multi-objective closed-loop optimization framework is formed with minimizing carbon emissions throughout the entire life cycle as the objective function and multiple performance indicators as constraints. A genetic algorithm is used to solve for the globally optimal control parameters and feed back to update the control strategy, achieving an organic unity of climate adaptation, extreme protection, and low-carbon optimization.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a dynamic photovoltaic control method adapted to regional climate, the method comprising the following specific steps: S1: Collect historical meteorological data and meteorological early warning rules for the target area, and classify the trigger thresholds for normal climate conditions and severe weather conditions; the normal climate conditions include summer conditions, winter conditions, and transitional season conditions, and the severe weather conditions include typhoon conditions and rainstorm conditions. S2: Based on the collected conventional climate conditions parameters, match the preset seasonal control strategy library, dynamically adjust the arrangement, spatial form and opening and closing status of photovoltaic modules, and construct photovoltaic building skin effects that adapt to different seasons. S3: Real-time monitoring of meteorological early warning data. When a typhoon or rainstorm severe weather trigger signal is detected, an emergency protection strategy is activated to retract, close, lock and reinforce the photovoltaic module, change the overall layout structure of the photovoltaic skin, and achieve all-round protection of photovoltaic materials. S4: Construct a carbon emission simulation model for the entire life cycle of a building, and perform carbon emission simulation calculations for different photovoltaic control schemes. Simulate carbon emission data for the entire stages of building material production, construction, operation, and demolition, and extract the total carbon emissions, average annual carbon emission intensity, and cumulative carbon emission reduction of the photovoltaic system for each scheme. S5: Taking the minimization of carbon emissions throughout the building's life cycle as the objective function, and using photovoltaic power generation, building air conditioning energy consumption, module regulation energy consumption, and module maintenance costs as constraints, the optimal combination of control parameters is solved through a genetic algorithm. The optimal parameters are then fed back to the seasonal regulation strategy library and emergency protection strategy to iteratively optimize the overall regulation scheme.

[0006] Furthermore, in S2, the preset seasonal control strategy library includes control strategies: summer control uses a shading and heat dissipation type arrangement, winter control uses a light-transmitting and heat-generating type arrangement, and transitional season control uses a balanced and adaptive type arrangement. In the shading and heat dissipation type arrangement, the photovoltaic modules are controlled to adjust to a large tilt angle, increase the horizontal spacing of the modules, and adopt a staggered arrangement. In this configuration, the modules form a continuous horizontal shading structure, blocking direct solar radiation from entering the room and reducing the building's air conditioning cooling load. At the same time, ventilation gaps are reserved between the modules, and the thermal pressure effect drives the airflow between the skins to enhance the heat dissipation effect. In the light-transmitting solar thermal power generation configuration, the photovoltaic modules are controlled to have a small tilt angle, the horizontal spacing between the modules is reduced, and a close vertical arrangement is adopted. In this configuration, the photovoltaic modules are adapted to the low solar altitude angle in winter, maximizing the amount of solar radiation received and improving the photovoltaic power generation efficiency. At the same time, the light transmittance of the gaps between the modules is controlled to allow an appropriate amount of solar radiation to enter the room, improving the building's passive solar heat gain and reducing heating energy consumption. In the balanced and adaptable arrangement, the photovoltaic modules are controlled to be adjusted to a medium tilt angle, and the module spacing and opening degree are between the summer and winter configurations, using a diagonally staggered arrangement. This configuration can balance photovoltaic power generation efficiency, natural ventilation and building lighting. It guides outdoor airflow into the room through the gaps between the modules to achieve passive cooling and reduce the operating time of air conditioning and heating equipment.

[0007] Furthermore, when the typhoon condition is triggered, all photovoltaic modules are driven to quickly retract into a closed state completely parallel to the building facade. Simultaneously, a multi-point mechanical locking mechanism is activated to reinforce the modules, forming a continuous closed surface for the entire photovoltaic skin. This significantly reduces the wind resistance coefficient, weakens the frontal impact and negative pressure suction effect of strong winds on the photovoltaic modules, and prevents module deformation, detachment, and structural damage. When the rainstorm condition is triggered, the photovoltaic modules are driven to adjust to a closed posture with a preset water-guiding angle, ensuring precise alignment of the guide channels at the edge of the modules to form a continuous drainage slope. This guides rainwater to drain quickly along a preset drainage path, preventing rainwater from accumulating on the surface and seeping into the photovoltaic module junction box and building envelope. This protects the photovoltaic electrical components and the building exterior walls, reducing the risk of leakage and corrosion. After the severe weather is lifted and meteorological parameters return to a safe threshold, the system automatically unlocks and returns to the normal control mode corresponding to the current season.

[0008] Furthermore, S4 specifically includes: Based on the life cycle assessment theory, the accounting boundaries and functional units are defined, and a carbon emission accounting model covering four stages, namely building material production, construction and installation, operation and maintenance, and demolition and recycling, is constructed. The photovoltaic control parameters, building foundation parameters, and environmental and economic parameters of photovoltaic configuration in each season are uniformly input into the simulation model. The model performs carbon emission quantification calculations in four stages in sequence. In the operation stage, the changes in building air conditioning energy consumption caused by the shading and heat collection effects of the photovoltaic skin, the carbon emission reduction generated by photovoltaic power generation replacing grid electricity, and the increase in carbon emissions from operation and maintenance energy consumption caused by module adjustment and protection actions are simultaneously coupled and calculated. After the simulation is completed, the total carbon emissions throughout the entire life cycle are automatically summarized, and the annual average carbon emission intensity, cumulative carbon emission reduction of photovoltaic system, and carbon emission intensity derived indicators per unit of power generation are calculated. The carbon emission ratio and sub-item details of each stage are output simultaneously. Using fixed photovoltaic schemes as the benchmark group, the simulation results are compared with industry standard benchmark values ​​to verify the accuracy of model calculations and ensure that the simulation results of each control scheme are reliable and comparable.

[0009] Furthermore, the photovoltaic control parameters include pitch angle, module spacing, arrangement, and opening / closing degree for summer, winter, and transition seasons; action logic for typhoon and rainstorm protection; annual average trigger frequency; and energy consumption parameters of the locking mechanism. The building foundation parameters include the facade area, window-to-wall ratio, thermal parameters of the building envelope, and indoor thermal environment design parameters of the target building. The environmental and economic parameters include local meteorological data, regional power grid carbon emission factors, material transportation distance, construction machinery parameters, and operation and maintenance cycle parameters.

[0010] Furthermore, the formulas for quantifying carbon emissions in the four stages are as follows: Building material production stage: ; Construction and installation phase: ; Operation and maintenance phase: ; Demolition and Recycling Phase: ; in, The number of types of quality building materials. This represents the number of types of area-type components. This refers to the number of types of construction machinery. The number of types of installation consumables. The number of types of demolition machinery. This represents the number of types of recyclable materials. It is the first Total usage of similar quality building materials It is the first Carbon emission factors of similar quality building materials It is the first The total area of ​​area-type components, It is the first Carbon emission factor per unit area of ​​area-type components, It is the first Total number of shifts for this type of construction machinery It is the first Carbon emission factors per shift for construction machinery It is the first Total usage of installation consumables It is the first Carbon emission factors of installation consumables It is the total transported mass of building materials. This is the average transportation distance for building materials. It is a carbon emission factor in the road transportation of building materials. It is the total annual energy consumption for operation, maintenance and regulation of the photovoltaic system. This refers to the total annual air conditioning energy consumption of a building under the influence of photovoltaic panels. This refers to the total annual power generation of the photovoltaic system. It is the carbon emission factor of the power grid in the project location. It refers to the design service life of the photovoltaic system throughout its entire life cycle; It is the first Total number of shifts for demolition machinery It is the first Carbon emission factors per shift for demolition machinery, It is the total transport weight of demolition waste. It is the average transportation distance of waste. It is a carbon emission factor in the road transport of waste. It is the total amount of recyclable material of type r that is recovered. It is the carbon emission replacement factor for the recycling of the r-th type of material.

[0011] Furthermore, in S5, the objective function is to minimize the carbon emissions throughout the building's life cycle. The objective function is: Wherein, LCCE represents the total carbon emissions over the entire lifecycle of a building facade photovoltaic system. This represents the total carbon emissions during the building materials production stage. This represents the total carbon emissions during the construction and installation phase. For the total net carbon emissions during the operation and maintenance phase. This represents the total net carbon emissions during the dismantling and recycling phase.

[0012] Furthermore, in S5, the constraint condition is expressed as: ,in, It is the preset minimum threshold for annual power generation. It is the preset annual maximum threshold for air conditioning energy consumption. It is the total annual energy consumption of the drive mechanism, belonging to Sub-items; It is a preset annual energy consumption limit. It is the total maintenance cost per unit area over the entire life cycle. It is a preset upper limit for maintenance cost per unit area.

[0013] Furthermore, in S5, the optimal control parameter vector to be solved is: ,in, , , These represent the tilt angles of the photovoltaic modules in summer, winter, and transitional seasons, respectively. , , These represent the horizontal spacing of photovoltaic modules for summer, winter, and transitional seasons, respectively. , These are the temperature thresholds for switching between summer and winter operating conditions. It is the wind speed threshold triggered by typhoon conditions. It is the rainfall threshold triggered by heavy rain conditions. It is the temperature switching hysteresis range. This is the hysteresis interval of wind speed switching, and the solution is obtained using a genetic algorithm with a fitness function. Iterative optimization is performed, and after selection, crossover, and mutation operations, the globally optimal parameter combination that satisfies all constraints is output. It is an adaptive function. To calculate the total carbon emissions by substituting the control parameter vector X into the constructed life-cycle carbon emission simulation model, P is a penalty coefficient used to penalize infeasible solutions that do not meet the constraints. Let q be the violation function of the q-th constraint, where q = 1, 2, 3, 4 correspond to the annual power generation constraint, annual air conditioning energy consumption constraint, annual regulation energy consumption constraint, and life-cycle maintenance cost constraint, respectively. When a certain constraint is satisfied... ≤0, when violated >0.

[0014] Compared with existing technologies, this dynamic photovoltaic control method adapted to regional climate has the following advantages: This invention establishes a multi-condition hierarchical judgment mechanism adapted to the meteorological characteristics of the target region through climate condition calibration, enabling precise matching of photovoltaic skin control strategies with local climate patterns and improving the regional applicability of the solution. Through severe weather photovoltaic protection control, the photovoltaic modules are actively closed and locked to reinforce them under typhoon and rainstorm conditions, reducing wind resistance coefficient and rainwater erosion risk, effectively extending the service life of the components, and improving the structural safety and operational reliability of the system. Through full life cycle carbon emission simulation modeling, a standardized accounting system and a unified dimensional calculation formula system are established to achieve refined and comparable quantification of carbon emission reduction benefits, providing reliable data support for solution evaluation. Through multi-objective closed-loop optimization, a mathematical optimization framework centered on minimizing full life cycle carbon emissions is constructed, transforming carbon emission reduction from a qualitative goal into a quantitative constraint. The global optimal control parameters are solved through a genetic algorithm and feedback is used to correct the control strategy, ensuring the carbon reduction optimization of the control scheme, and ultimately achieving a unity of climate adaptability, extreme protection, and low-carbon benefits.

[0015] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] Figure 1 A flowchart of a dynamic photovoltaic control method adapted to regional climate; Figure 2 This is a flowchart of step S4 of a dynamic photovoltaic control method adapted to regional climate. Figure 3 This is a flowchart of step S5 of a dynamic photovoltaic control method adapted to regional climate. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0019] Reference Figure 1 This invention proposes a dynamic photovoltaic control scheme adapted to regional climate. It adopts a data processing and control mechanism that uses climate condition classification calibration and dynamic regulation of multi-form photovoltaic skin. It can achieve precise separation of seasonal climate adaptation and extreme weather protection without relying on fixed tilt angle photovoltaic arrangement, and implement high-precision quantitative optimization control of carbon emissions throughout the building's life cycle.

[0020] The solution described in this embodiment specifically includes: S1: Collect historical meteorological data and meteorological early warning rules for the target area, and classify the trigger thresholds for normal climate conditions and severe weather conditions; the normal climate conditions include summer conditions, winter conditions, and transitional season conditions, and the severe weather conditions include typhoon conditions and rainstorm conditions. S2: Based on the collected conventional climate conditions parameters, match the preset seasonal control strategy library, dynamically adjust the arrangement, spatial form and opening and closing status of photovoltaic modules, and construct photovoltaic building skin effects that adapt to different seasons. S3: Real-time monitoring of meteorological early warning data. When a typhoon or rainstorm severe weather trigger signal is detected, an emergency protection strategy is activated to retract, close, lock and reinforce the photovoltaic module, change the overall layout structure of the photovoltaic skin, and achieve all-round protection of photovoltaic materials. S4: Construct a carbon emission simulation model for the entire life cycle of buildings, and perform carbon emission simulation calculations for different photovoltaic control schemes. Simulate carbon emission data for the entire stages of building material production, construction, operation, and demolition, and extract the total carbon emissions, average annual carbon emission intensity, and photovoltaic low-carbon emission reduction indicators for each scheme. S5: Taking the minimization of carbon emissions throughout the building's life cycle as the objective function, and using photovoltaic power generation, building air conditioning energy consumption, module regulation energy consumption, and module maintenance costs as constraints, the optimal combination of control parameters is solved through a genetic algorithm. The optimal parameters are then fed back to the seasonal regulation strategy library and emergency protection strategy to iteratively optimize the overall regulation scheme.

[0021] Specifically, this invention decomposes the photovoltaic skin regulation requirements into two independent paths: conventional seasonal adaptation and severe weather protection. Through parallel seasonal morphology regulation links and emergency protection links, independent modeling and tracking are implemented for building thermal performance optimization and structural safety protection, respectively. The seasonal morphology regulation link divides the working conditions into three categories: summer, winter, and transitional seasons based on local meteorological patterns, and generates three skin morphologies: shading heat dissipation type, light-transmitting heat collection and power generation type, and balanced adaptation type, to achieve synergistic optimization of photovoltaic power generation efficiency and building air conditioning energy consumption. The emergency protection link triggers closing and drainage actions for extreme working conditions such as typhoons and rainstorms to reduce wind resistance coefficient and rainwater erosion risk. The full life cycle carbon emission simulation model generates quantitative results of carbon emissions of each scheme on the full time scale and separates them from the single power generation evaluation system to obtain a comprehensive low-carbon benefit index. Through multi-constraint optimization using genetic algorithms, mutual verification is achieved in the parameter space dimension to realize accurate identification of control parameter combinations.

[0022] In S1, the hourly meteorological observation dataset of the target area over the past ten years is retrieved, and four core parameters are extracted: daily average temperature, daily maximum wind speed, daily rainfall, and total horizontal radiation. A meteorological feature database is established, outliers are removed, and missing data are interpolated. K-means clustering analysis was performed on the daily average temperature series. The number of clusters, k, was determined using the elbow rule combined with the silhouette coefficient method. The k value corresponding to the inflection point of the sum of squares within the cluster and the local maximum of the silhouette coefficient was selected. In this scheme, k=3 was set. The clustering output three types of temperature center values, corresponding to summer conditions, transitional season conditions, and winter conditions in descending order of temperature. and Calculate the seasonal operating condition temperature boundary, where The temperature threshold for switching to summer operating conditions. The temperature threshold for switching to winter operating conditions. This represents the average daily temperature of the high-temperature cluster. This represents the average daily temperature of the low-temperature cluster. For the standard deviation of the temperature of the high-temperature cluster, The standard deviation of the temperature of the low-temperature cluster. The boundary adjustment coefficient α is determined based on a combination of the local daily temperature fluctuation range and the annual average operating condition switching frequency target, with a value ranging from 0.3 to 0.7. Furthermore, the value of α must satisfy the following conditions: To ensure that the summer switching threshold is higher than the winter switching threshold and to avoid the failure of the judgment logic, a larger value is taken in areas with large temperature fluctuations to reduce false switching, and a smaller value is taken in areas with stable temperatures to improve response sensitivity. The calculated temperature boundary values ​​of various clusters are used as the basic thresholds for seasonal operation condition switching. The determination of the transitional season operation condition is: when the real-time daily average temperature is simultaneously lower than the summer switching threshold and higher than the winter switching threshold, it is the transitional season operation condition. The wind speed thresholds corresponding to the blue, yellow, and orange typhoon warnings issued by the local meteorological department, as well as the hourly rainfall thresholds corresponding to the yellow and orange rainstorm warnings, are collected. Combined with the wind load design standards of the building envelope and the wind resistance level of the photovoltaic modules, a safety factor of 1.2 is used to determine the trigger wind speed for typhoon conditions. The typhoon safety factor is determined comprehensively based on the building structure safety level and the salt spray corrosion level of the photovoltaic modules, with a value range of 1.1 to 1.5, and the upper limit is used in coastal areas with high corrosion. The rainfall trigger for rainstorm conditions is determined comprehensively based on the design return period of the roof drainage system and the waterproof level of the photovoltaic junction box, and is usually taken as 0.8 times the hourly rainfall that occurs once every five years. To avoid mechanical wear caused by frequent switching of operating conditions, all thresholds are configured with corresponding hysteresis intervals. The proportion of the hysteresis interval is determined based on the design life of the drive mechanism and the annual regulation energy consumption constraint, and is usually taken as 5% to 15% of the threshold. When the mechanism life is tight or the energy consumption constraint is strict, a larger value is taken.

[0023] In step S2, the seasonal control strategy matching includes: pre-setting three skin morphology parameter groups: summer sunshade and heat dissipation type, winter light transmission and heat collection and power generation type, and transitional season balanced adaptation type; real-time collection of outdoor daily average temperature, comparison with the seasonal operating condition threshold defined in step one, and determination of the current operating condition; based on the determination result, calling the corresponding morphology parameter group and issuing pitch and lateral spacing adjustment commands to the drive mechanism; after adjustment, collecting the actual position feedback value of each module, comparing it with the target parameters, and triggering secondary calibration when the deviation exceeds the allowable range.

[0024] Specifically, a preset seasonal control strategy library stores three sets of morphological parameters. Each set includes four core parameters: pitch angle, lateral spacing, arrangement method, and opening / closing degree. Under these three morphologies, the effective solar radiation area is determined according to... Calculation, where The effective light-receiving area per unit facade area. This refers to the nominal area of ​​a single photovoltaic module. The solar altitude angle, The tilt angle of the photovoltaic module (the angle between the module and the vertical direction). The area utilization rate corresponding to the layout method is denoted by , which is a function of the horizontal spacing d of the modules. The system collects the daily average outdoor temperature in real time, compares it with the defined seasonal operating condition threshold to determine the current operating condition, and then calls up the corresponding morphological parameter group and sends adjustment commands to the drive mechanism. For summer shading and heat dissipation configuration control: the tilt angle of the photovoltaic modules is determined based on the local noon solar altitude angle on the summer solstice, with a shading margin of 5°-10° subtracted from the solar altitude angle to make the module plane nearly horizontal; the lateral spacing is calculated based on the module width and shadow length to ensure that the shadows of upper and lower modules do not overlap at noon, while reserving ventilation gaps; the arrangement adopts a staggered vertical arrangement, so that the lower edge of the module blocks the window area above the lower module, forming a continuous horizontal shading structure, blocking high-angle direct sunlight from entering the room in summer, reducing the air conditioning cooling load, and the ventilation gaps reserved between modules use thermal pressure to drive airflow in the skin layer, enhancing the heat dissipation effect, reducing the operating temperature of the photovoltaic modules, and improving power generation efficiency.

[0025] For winter solar thermal power generation, the photovoltaic module tilt angle is determined based on the local noon solar altitude angle on the winter solstice, minimizing the angle between the module's normal direction and the incident light, making the module plane nearly vertical. The lateral spacing is reduced to 1.0 to 1.1 times the module width, with vertically arranged closely side-by-side. This ensures the module's light-receiving surface faces the low-angle solar radiation in winter, maximizing direct sunlight reception and increasing photovoltaic power generation. Simultaneously, the light transmittance between module gaps is controlled between 15% and 25%, allowing appropriate diffused radiation to enter the room, increasing passive solar heat gain and reducing heating energy consumption. For the transitional season balanced adaptation form control: the tilt angle of the photovoltaic modules is taken as the weighted average of the tilt angles in summer and winter, and the weight is determined according to the length of the transitional season and the proportion of energy consumption contribution. The module spacing and opening degree are between the summer and winter forms, and a diagonal staggered arrangement is adopted to take into account the photovoltaic power generation efficiency, natural ventilation and indoor lighting. Outdoor airflow is guided into the room through the module gaps to achieve passive cooling and shorten the operating time of air conditioning and heating equipment. The selection of the arrangement method aims to optimize the building's annual air conditioning energy consumption and photovoltaic power generation, and is determined through iterative verification using energy consumption simulation software.

[0026] After adjustment, the actual position feedback values ​​of each module are collected and compared with the target parameters. If the deviation exceeds the allowable range, a secondary calibration is triggered to ensure the accuracy of the shape adjustment.

[0027] In S3, the emergency protection and control for severe weather includes: real-time access to local meteorological early warning data and on-site wind speed and rainfall sensor data; when any data source triggers the severe weather threshold, the corresponding protection action is initiated; under typhoon conditions, the closing and multi-point locking sequence is executed; under heavy rain conditions, the water guiding attitude adjustment and drainage path alignment sequence is executed; after the severe weather is lifted and the parameters fall below the safety threshold and remain below the set time, the unlocking and reset are executed to restore the normal form of the current season.

[0028] Specifically, severe weather conditions are monitored in parallel through two data sources: one source connects to typhoon and rainstorm warning signals pushed by the meteorological early warning information release platform, providing early warning information with a large lead time; the other source connects to real-time data from ultrasonic anemometers and tipping bucket rain gauges installed on the building roof, providing on-site measured data. Both signals are triggered by OR logic; if either reaches a threshold, the protection program is activated to ensure timely response. The total wind load on the photovoltaic surface under typhoon conditions is also monitored. according to Calculate, where, For wind load shape coefficient, This is the wind pressure height variation coefficient. Basic wind pressure, The total area of ​​the photovoltaic skin is used. The wind load shape coefficient is taken according to the relevant regulations corresponding to the shape coefficient of the building envelope. It is 1.8 for the open state and 0.85 for the closed state. Important buildings or super high-rise buildings need to be verified and corrected by wind tunnel tests. The wind pressure height variation coefficient is determined by referring to a table based on the landform category and building height. For Class B landforms, it is calculated with a power exponent of 0.15. The basic wind pressure is the wind pressure value corresponding to the local 10-minute average maximum wind speed. When the typhoon condition is triggered, the drive mechanism drives all photovoltaic modules to quickly retract to a closed state that is completely parallel to the building facade. The pitch angle is zeroed, the module gap is reduced to the minimum, and after closing, the multi-point mechanical locking mechanism is activated simultaneously. The number of locking points is based on the total wind load and the design bearing capacity of a single point. The reverse calculation determines that the modules are simultaneously locked at three locations—the top and bottom ends and the middle—according to the principle of uniform distribution. This creates a continuous, closed surface for the entire photovoltaic skin, reducing the wind resistance coefficient from 1.8 in the open state to below 0.9 in the closed state. This significantly weakens the frontal impact and negative pressure suction effect of strong winds on the photovoltaic modules. When a rainstorm condition is triggered, the photovoltaic modules are driven to adjust to a closed posture with a preset water guide angle. The water guide angle is determined based on the drainage slope requirements of relevant regulations and the structural angle of the component's guide channel, typically a 10°-15° downward angle. This ensures that the guide channels at the edge of the module are precisely aligned, forming a continuous drainage slope from top to bottom. This guides rainwater along the preset drainage path to quickly flow into the roof rainwater hopper, preventing rainwater from accumulating on the surface and seeping into the junction box and building envelope.

[0029] The severe weather release determination adopts a hysteresis mechanism. The hysteresis duration is determined based on the average duration and intermittent characteristics of severe weather in local meteorological statistics, usually 30 minutes. After the meteorological parameters fall below the safe threshold and remain below this duration, the system automatically unlocks the locking mechanism and restores the normal control mode corresponding to the current season.

[0030] In S4, such as Figure 2As shown, the full life cycle carbon emission simulation calculation includes: defining the accounting boundary and functional units based on life cycle assessment theory; constructing a carbon emission accounting model covering four stages: building material production, construction and installation, operation and maintenance, and demolition and recycling; inputting photovoltaic control parameters, building foundation parameters, and environmental and economic parameters into the simulation model; performing carbon emission quantification calculations for the four stages in sequence; summarizing the total carbon emissions throughout the entire life cycle, calculating derived indicators, and outputting detailed breakdowns.

[0031] Specifically, the accounting boundary is defined based on the life cycle assessment standard, covering the entire chain of photovoltaic skin system building material raw material mining, production and manufacturing, transportation, on-site construction and installation, operation and maintenance, final demolition and waste disposal, excluding carbon emissions from the main building structure itself and indoor facilities. The functional unit is set as the carbon dioxide equivalent emissions per square meter of building skin within the 25-year design service life. The design service life is determined by combining the linear power warranty period of crystalline silicon photovoltaic modules and the building design reference period, usually taken as 25 years, which can be extended to 30 years for BIPV integrated products.

[0032] The simulation model input parameters are divided into three categories: photovoltaic control parameters, building foundation parameters, and environmental and economic parameters. The photovoltaic control parameters include pitch angle, module spacing, arrangement, and opening degree for summer, winter, and transitional seasons, as well as the action logic for typhoon and rainstorm protection, the average annual trigger frequency, and the energy consumption parameters of the locking mechanism. The building foundation parameters include the facade area, window-to-wall ratio, thermal parameters of the building envelope, and indoor thermal environment design parameters of the target building. The environmental and economic parameters include local meteorological data, regional power grid carbon emission factors, material transportation distance, construction machinery parameters, and operation and maintenance cycle parameters.

[0033] Calculate the implicit carbon emissions of all photovoltaic modules, support structures, drive mechanisms, electrical cables, and control systems, according to... Calculation, where For the total usage of the i-th type of quality building materials, For the i-th type of quality-type building materials carbon emission factor, For the total area of ​​the j-th type of area-type component, denoted as the carbon emission factor per unit area for the j-th type of area-type component.

[0034] The carbon emissions from construction machinery operation shifts, installation material consumption, and building material transportation are categorized into three groups, according to... Calculation, where Let k be the total number of shifts for construction machinery of type k. For the k-th type of construction machinery shift carbon emission factor, This represents the total usage of installation consumables for Category I. The carbon emission factor for Category I installation consumables is given by M, where M is the total transport mass of building materials and D is the average transport distance of building materials. This refers to the carbon emission factor for road transportation of building materials. The transportation distance is determined based on the actual distance between the building material production site and the project site; when no specific data is available, the industry default average transportation distance of 150km is used.

[0035] The calculation is categorized into three types: energy consumption for operation, maintenance, and regulation of photovoltaic systems; energy consumption for building air conditioning; and carbon emission reductions generated by photovoltaic power generation replacing grid electricity. Calculation, where The total annual energy consumption for operation, maintenance, and regulation of the photovoltaic system. The total annual air conditioning energy consumption of a building under the influence of photovoltaic panels. This represents the total annual power generation of the photovoltaic system. The carbon emission factor of the power grid at the project site is denoted as n, and the design service life of the photovoltaic system is denoted as n. The air conditioning energy consumption is calculated by coupling the shading and heat collection effects of the photovoltaic skin with building energy consumption simulation software. The photovoltaic power generation is calculated based on hourly meteorological data and module tilt angle. The power grid carbon emission factor adopts the regional power grid average emission factor published annually by the Ministry of Ecology and Environment.

[0036] Calculate the carbon emissions from dismantling machinery and waste transportation, and deduct the emission reductions from the substitution of recyclable materials, according to... Calculation, where For the total number of shifts of demolition machinery of type p, For the carbon emission factor of the dismantling machinery shift in category p, The total transport weight of the demolition waste, The average transport distance for waste. Carbon emissions from waste road transport For the total amount of recyclable materials of type r, The recycling substitution factor is for Category r materials. The recycling substitution factor is determined based on the difference between the carbon emissions from the production of virgin materials and the carbon emissions from recycling and processing. For high-recyclable materials such as aluminum alloys and steel, the factor is calculated based on the industry average recycling rate.

[0037] After the calculations for the four stages are completed, the total carbon emissions over the entire life cycle are obtained by summing them up, and the annual average carbon emission intensity, cumulative carbon emission reduction, and carbon emission intensity per unit of electricity generation are calculated.

[0038] In S5, such as Figure 3 As shown, the optimal control parameter vector is defined, and the upper and lower bounds of each parameter value are clarified; a fitness function is constructed with the goal of minimizing carbon emissions throughout the entire life cycle; genetic parameters such as population size, number of iterations, crossover probability, and mutation probability are set; after initializing the population, selection, crossover, and mutation operations are performed generation by generation; the fitness value of each individual in each generation is substituted into the simulation model to verify the constraints; after the iteration terminates, the globally optimal parameter combination that satisfies all constraints is output; the optimal parameters are written back to the seasonal regulation strategy library and emergency protection strategy to complete one round of iterative optimization.

[0039] Specifically, the control parameter vector to be optimized is defined, which has 12 dimensions and is represented as follows: ,in , , These represent the tilt angles of the photovoltaic modules in summer, winter, and transitional seasons, respectively. , , These represent the horizontal spacing of photovoltaic modules for summer, winter, and transitional seasons, respectively. , These are the temperature thresholds for switching between summer and winter operating conditions. The wind speed threshold triggered by typhoon conditions. The rainfall threshold is triggered for heavy rain conditions. This is the temperature switching hysteresis range. For the wind speed switching hysteresis range, all parameters are set with physically feasible upper and lower bounds: pitch angle range 0°-90°, determined based on mechanical structure limits; lateral spacing range 0.8-2 times the module width, determined based on the span of the support structure and the minimum installation gap; temperature threshold range -10 to 40℃, determined based on extreme temperature statistics; wind speed threshold range 10-50m / s, determined based on the Beaufort wind force rating and the structure's wind resistance rating; rainfall threshold range 10-100mm / h, determined based on rainfall intensity classification; the hysteresis range is taken as 5% to 15% of the corresponding threshold, determined based on the mechanism's lifespan and energy consumption constraints.

[0040] The objective function, guided by minimizing total carbon emissions over the entire life cycle, is expressed as follows: Where LCCE represents the total carbon emissions over the entire lifecycle of a building facade photovoltaic system. This represents the total carbon emissions during the building materials production stage. This represents the total carbon emissions during the construction and installation phase. For the total net carbon emissions during the operation and maintenance phase. This represents the total net carbon emissions during the dismantling and recycling phase.

[0041] The constraints include: ,in The minimum threshold for annual power generation is set based on the project's green building star rating or carbon emission reduction target. The maximum annual air conditioning energy consumption threshold is set based on building energy conservation design standards. The total annual energy consumption for the drive mechanism is [not specified]. Sub-item, The annual energy consumption limit is set based on the system energy efficiency index. The total maintenance cost per unit area over the entire lifecycle. The upper limit of maintenance cost per unit area is set based on the project's full life cycle cost budget.

[0042] The fitness function takes the negative value of the total carbon emissions over the entire life cycle and adds a constraint penalty term, expressed as follows: Where P is the penalty coefficient. Let $\frac{q}{q}$ be the violation amount of the constraint. Higher fitness corresponds to lower total carbon emissions. The penalty coefficient $P$ is taken as 10 to 100 times the LCCE value of the baseline solution to ensure that the fitness of infeasible solutions is significantly lower than that of feasible solutions. During initialization, an initial population is randomly generated within the upper and lower bounds of the parameters. The population size is determined according to the parameter dimension. During the iteration process, selection, crossover, and mutation operations are performed generation by generation: the selection operation adopts the tournament selection method, with a tournament size of 3. Three individuals are randomly selected each time, and the one with the highest fitness is retained to enter the next generation; the crossover operation adopts arithmetic crossover with real number encoding. The crossover probability is determined according to the balance between the parameter dimension and the convergence speed, and is usually taken as 0.7-0.9; the mutation operation adopts Gaussian mutation, and the mutation probability is usually taken as 0.05-0.15. A normal distribution random perturbation is superimposed on the individual parameters. The standard deviation of the perturbation decreases adaptively with the iteration generation. All individuals in each generation are substituted into the simulation model to calculate the carbon emissions of the entire life cycle, and the four constraints are checked one by one. Individuals that do not meet the constraints are subject to a penalty function, which significantly reduces their fitness.

[0043] The iteration termination condition adopts a dual criterion: the maximum number of iterations is reached, or the improvement of the optimal fitness is less than the threshold for 20 consecutive generations, the iteration terminates early. The maximum number of generations is determined based on the parameter dimension and computing resources, usually 100-500 generations. After the iteration terminates, the globally optimal parameter combination is output. The optimal parameters directly update the morphological parameters and switching thresholds in the seasonal regulation strategy library, as well as the trigger thresholds in the emergency protection strategy, to complete the closed-loop optimization.

[0044] Example 1 This embodiment is applied to high-rise buildings in hot summer and warm winter regions of southern China, where summers are long, hot and humid, and winters are mild and short, resulting in air conditioning energy consumption accounting for a high proportion of the building's total energy consumption.

[0045] Hourly meteorological data for the region over the past ten years were retrieved, and K-means clustering was performed on the daily average temperature with k=3. The resulting temperature center was 28.5℃ in summer, 20.2℃ in the transition season, and 13.8℃ in winter. The boundary adjustment coefficient α was set to 0.4, and the summer switching threshold was calculated to be 26.3℃ and the winter switching threshold to be 16.1℃. The typhoon triggering wind speed was set to 32m / s, corresponding to the lower limit of level 12 wind force, and a safety factor of 1.2 was added. The rainstorm triggering rainfall was set to 50mm / h, corresponding to the orange warning threshold. The hysteresis interval ratio was set to 10% to prevent frequent switching during the spring-summer transition period.

[0046] In summer, the tilt angle is set at 68°, close to horizontal to enhance shading. The lateral spacing is 1.6 times the module width, and the modules are arranged in a staggered manner, achieving a shading coefficient of 0.72 and reducing the air conditioning cooling load by about 18%. In winter, the tilt angle is set at 15°, close to the vertical facade to maximize sunlight. The lateral spacing is 1.05 times the module width, and the modules are arranged closely vertically, increasing power generation by about 22% in winter. In the transition season, the tilt angle is set at 42°, and the modules are arranged diagonally in a staggered manner to balance ventilation and lighting.

[0047] The region experiences an average of 3 to 4 typhoons per year, with heavy rainfall. Under typhoon closure conditions, the wind load shape coefficient drops from 1.8 to 0.85, with three points simultaneously locked to withstand instantaneous gusts of up to 40 m / s. The rainwater drainage angle is set at 12°, and the diversion channels are aligned to form a continuous drainage slope, suitable for short-term heavy rainfall.

[0048] The entire life cycle is calculated over 25 years. The carbon emission factor of the power grid is taken as 0.58 kgCO2 / kWh for the South China regional power grid. The carbon emission during the operation phase accounts for about 62%, of which the energy consumption of air conditioning in summer accounts for 55% of the carbon emission during operation.

[0049] After 300 generations of iteration, the genetic algorithm outputs the optimal parameters. The optimized parameters reduce carbon emissions over the entire life cycle by 14.7% compared to the fixed tilt angle scheme, increase annual power generation by 9.3%, reduce annual air conditioning energy consumption by 12.1%, and control annual regulation energy consumption to within 1.2% of total power generation.

[0050] Example 2 This embodiment is applied to public buildings in cold northern regions, which are characterized by long, cold winters with high heating energy consumption and relatively concentrated hot summers.

[0051] Hourly meteorological data for the region over the past ten years were retrieved. K-means clustering yielded a summer temperature center of 26.8℃, a transitional season temperature of 14.5℃, and a winter temperature of 2.3℃. With a boundary adjustment coefficient α of 0.5, the summer switching threshold was calculated to be 23.6℃, ​​and the winter switching threshold to be 5.8℃. The typhoon risk in this region is low, with a typhoon triggering wind speed of 28 m / s and a heavy rain triggering rainfall of 40 mm / h.

[0052] In summer, the tilt angle is set at 62°, the lateral spacing is 1.5 times the module width, and staggered shading reduces the air conditioning cooling load by about 15%. In winter, the tilt angle is set at 20°, the lateral spacing is 1.0 times the module width, and the modules are arranged closely vertically. The light transmittance between modules is controlled at 20%, and passive solar heat gain reduces heating energy consumption by about 11%, while increasing power generation by 18% in winter. In the transition season, the tilt angle is set at 41° for balanced adaptation.

[0053] The region experiences frequent strong winds in winter. Although typhoon conditions are infrequent, protection against strong winter winds still needs to be taken into account. The wind resistance coefficient in the closed state is reduced to 0.88, and the locking mechanism is verified based on the maximum winter wind speed. The rainwater guide angle is set at 10° to suit the rainfall intensity in the north.

[0054] The carbon emission factor of the power grid is taken as 0.88 kgCO2 / kWh for the North China power grid. The carbon emission reduction benefits during the heating season are significant. During the operation phase, the passive heat gain in winter accounts for 43% of the total annual emission reduction.

[0055] The genetic algorithm converged after 250 generations of iteration. The optimized full life cycle carbon emissions were reduced by 16.2% compared with the fixed tilt angle scheme, with carbon emission reduction during the winter heating period accounting for 43% and annual power generation increasing by 11.5%.

[0056] Example 3 This embodiment is applied to industrial buildings in coastal areas prone to typhoons. These areas have a subtropical maritime climate with long summers, high temperatures, and are frequently affected by typhoons each year, making structural safety protection a top priority.

[0057] Hourly meteorological data for the region over the past ten years were retrieved, and K-means clustering yielded a summer temperature center of 27.9℃, a transitional season temperature of 21.4℃, and a winter temperature of 14.6℃. The boundary adjustment coefficient α was set to 0.45, the typhoon triggering wind speed was set to 35 m / s, and the safety factor was set to 1.3 to adapt to the high-wind environment along the coast. The rainstorm triggering rainfall was set to 60 mm / h, corresponding to the frequent occurrence of heavy rainfall.

[0058] The summer configuration focuses on shading, heat dissipation, and ventilation for cooling, with a pitch angle of 65° and a lateral spacing of 1.5 times the module width, enhancing thermal pressure ventilation to reduce the module's operating temperature; the winter configuration focuses on solar power generation, with a pitch angle of 18° and a close arrangement to increase power generation.

[0059] Under the closed typhoon configuration, the wind shape coefficient drops from 1.8 in the open configuration to 0.82. The three-point locking mechanism starts simultaneously and can withstand instantaneous gusts of 50 m / s. The rainwater guide angle is set to 12°, and the guide channels are aligned to form a continuous drainage slope, which is suitable for short-term heavy rainfall. In salt spray environments, the locking mechanism is made of stainless steel with a corrosion resistance level of C4.

[0060] Considering the corrosive environment of coastal salt spray, a higher value is chosen for maintenance cost constraints, and the design service life is calculated as 25 years. The proportion of anti-corrosion maintenance costs during the operation and maintenance phase is higher than that of inland projects.

[0061] After 200 generations of iteration, the genetic algorithm outputs the optimal parameters. The optimized carbon emissions over the entire life cycle are reduced by 12.8% compared to the fixed tilt angle scheme. The structural safety margin is increased by 37% under typhoon conditions, and the water tightness compliance rate is 100% under heavy rain conditions.

[0062] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A dynamic photovoltaic control method adapted to regional climate, characterized in that, The method includes the following specific steps: S1: Collect historical meteorological data and meteorological early warning rules for the target area, and classify the trigger thresholds for normal climate conditions and severe weather conditions; the normal climate conditions include summer conditions, winter conditions, and transitional season conditions, and the severe weather conditions include typhoon conditions and rainstorm conditions. S2: Based on the collected conventional climate conditions parameters, match the preset seasonal control strategy library, dynamically adjust the arrangement, spatial form and opening and closing status of photovoltaic modules, and construct photovoltaic building skin effects that adapt to different seasons. S3: Real-time monitoring of meteorological early warning data. When a typhoon or rainstorm severe weather trigger signal is detected, an emergency protection strategy is activated to retract, close, lock and reinforce the photovoltaic module, change the overall layout structure of the photovoltaic skin, and achieve all-round protection of photovoltaic materials. S4: Construct a carbon emission simulation model for the entire life cycle of a building, and perform carbon emission simulation calculations for different photovoltaic control schemes. Simulate carbon emission data for the entire stages of building material production, construction, operation, and demolition, and extract the total carbon emissions, average annual carbon emission intensity, and cumulative carbon emission reduction of the photovoltaic system for each scheme. S5: Taking the minimization of carbon emissions throughout the building's life cycle as the objective function, and using photovoltaic power generation, building air conditioning energy consumption, module regulation energy consumption, and module maintenance costs as constraints, the optimal combination of control parameters is solved through a genetic algorithm. The optimal parameters are then fed back to the seasonal regulation strategy library and emergency protection strategy to iteratively optimize the overall regulation scheme.

2. The dynamic photovoltaic control method adapted to regional climate according to claim 1, characterized in that, In S2, the preset seasonal control strategy library includes control strategies: summer control uses a shading and heat dissipation type arrangement, winter control uses a light-transmitting heat collection and power generation type arrangement, and transitional season control uses a balanced and adaptive type arrangement. In the sunshade and heat dissipation type arrangement, the photovoltaic modules are controlled to adjust to a large tilt angle, the horizontal spacing of the modules is increased, and a staggered arrangement is adopted. In the light-transmitting solar power generation configuration, the photovoltaic modules are controlled to have a small tilt angle, the horizontal spacing between the modules is reduced, and a close vertical arrangement is adopted. In the balanced and adaptable arrangement, the photovoltaic modules are controlled to adjust to a medium tilt angle, and the module spacing and opening degree are between the summer and winter configurations, using a diagonally staggered arrangement.

3. The dynamic photovoltaic control method adapted to regional climate according to claim 1, characterized in that, When the typhoon condition is triggered, all photovoltaic modules retract to a closed state parallel to the building facade, and the mechanical locking mechanism is activated to reinforce the modules at multiple points, so that the overall skin is a continuous closed surface; when the rainstorm condition is triggered, the photovoltaic modules adjust to a water-guiding closed posture, and the flow guiding structure at the edge of the module is aligned to form a continuous drainage slope, guiding rainwater to be discharged along a preset path.

4. The dynamic photovoltaic control method adapted to regional climate according to claim 1, characterized in that, S4 specifically includes: Based on the life cycle assessment theory, the accounting boundaries and functional units are defined, and a carbon emission accounting model covering four stages, namely building material production, construction and installation, operation and maintenance, and demolition and recycling, is constructed. The photovoltaic control parameters, building foundation parameters, and environmental and economic parameters of photovoltaic configuration in each season are uniformly input into the simulation model. The model performs carbon emission quantification calculations in four stages in sequence. In the operation stage, the changes in building air conditioning energy consumption caused by the shading and heat collection effects of the photovoltaic skin, the carbon emission reduction generated by photovoltaic power generation replacing grid electricity, and the increase in carbon emissions from operation and maintenance energy consumption caused by module adjustment and protection actions are simultaneously coupled and calculated. After the simulation is completed, the total carbon emissions over the entire life cycle are automatically summarized, and the annual average carbon emission intensity, cumulative carbon emission reduction of photovoltaic systems, and carbon emission intensity derived indicators per unit of electricity generation are calculated. The carbon emission ratio and sub-item details of each stage are output simultaneously.

5. The dynamic photovoltaic control method adapted to regional climate according to claim 4, characterized in that, The photovoltaic control parameters include tilt angle, module spacing, arrangement, and opening degree for summer, winter, and transition seasons; action logic for typhoon and rainstorm protection; average annual trigger frequency; and energy consumption parameters for locking mechanisms. The building foundation parameters include the facade area, window-to-wall ratio, thermal parameters of the building envelope, and indoor thermal environment design parameters of the target building. The environmental and economic parameters include local meteorological data, regional power grid carbon emission factors, material transportation distance, construction machinery parameters, and operation and maintenance cycle parameters.

6. The dynamic photovoltaic control method adapted to regional climate according to claim 4, characterized in that, The formulas for quantifying carbon emissions in the four stages are as follows: Building material production stage: ; Construction and installation phase: ; Operation and maintenance phase: ; Demolition and Recycling Phase: ; in, The number of types of quality building materials. This represents the number of types of area-type components. This refers to the number of types of construction machinery. The number of types of installation consumables. The number of types of demolition machinery. This represents the number of types of recyclable materials. It is the first Total usage of similar quality building materials It is the first Carbon emission factors of similar quality building materials It is the first The total area of ​​area-type components, It is the first Carbon emission factor per unit area of ​​area-type components, It is the first Total number of shifts for this type of construction machinery It is the first Carbon emission factors per shift for construction machinery It is the first Total usage of installation consumables It is the first Carbon emission factors of installation consumables It is the total transported mass of building materials. This is the average transportation distance for building materials. It is a carbon emission factor in the road transportation of building materials. It is the total annual energy consumption for operation, maintenance and regulation of the photovoltaic system. This refers to the total annual air conditioning energy consumption of a building under the influence of photovoltaic panels. This refers to the total annual power generation of the photovoltaic system. It is the carbon emission factor of the power grid in the project location. It refers to the design service life of the photovoltaic system throughout its entire life cycle; It is the first Total number of shifts for demolition machinery It is the first Carbon emission factors per shift for demolition machinery, It is the total transport weight of demolition waste. It is the average transportation distance of waste. It is a carbon emission factor in the road transport of waste. It is the total amount of recyclable material of type r that is recovered. It is the carbon emission replacement factor for the recycling of the r-th type of material.

7. The dynamic photovoltaic control method adapted to regional climate according to claim 6, characterized in that, In S5, the objective function is to minimize the carbon emissions throughout the building's life cycle. The objective function is: Wherein, LCCE represents the total carbon emissions over the entire lifecycle of a building facade photovoltaic system. This represents the total carbon emissions during the building materials production stage. This represents the total carbon emissions during the construction and installation phase. For the total net carbon emissions during the operation and maintenance phase. This represents the total net carbon emissions during the dismantling and recycling phase.

8. The dynamic photovoltaic control method adapted to regional climate according to claim 1, characterized in that, In S5, the constraint condition is expressed as follows: ,in, It is the preset minimum threshold for annual power generation. It is the preset annual maximum threshold for air conditioning energy consumption. It is the total annual energy consumption of the drive mechanism, belonging to Sub-items; It is a preset annual energy consumption limit. It is the total maintenance cost per unit area over the entire life cycle. It is a preset upper limit for maintenance cost per unit area.

9. A dynamic photovoltaic control method adapted to regional climate according to claim 1, characterized in that, In S5, the optimal control parameter vector to be solved is: ,in, , , These represent the tilt angles of the photovoltaic modules in summer, winter, and transitional seasons, respectively. , , These represent the horizontal spacing of photovoltaic modules for summer, winter, and transitional seasons, respectively. , These are the temperature thresholds for switching between summer and winter operating conditions. It is the wind speed threshold triggered by typhoon conditions. It is the rainfall threshold triggered by heavy rain conditions. It is the temperature switching hysteresis range. This is the hysteresis interval of wind speed switching, and the solution is obtained using a genetic algorithm with a fitness function. Iterative optimization is performed, and after selection, crossover, and mutation operations, the globally optimal parameter combination that satisfies all constraints is output. It is an adaptive function. To calculate the total carbon emissions by substituting the control parameter vector X into the constructed life-cycle carbon emission simulation model, P is a penalty coefficient used to penalize infeasible solutions that do not meet the constraints. Let q be the violation function of the q-th constraint, where q = 1, 2, 3, 4 correspond to the annual power generation constraint, annual air conditioning energy consumption constraint, annual regulation energy consumption constraint, and life-cycle maintenance cost constraint, respectively. When a certain constraint is satisfied... ≤0, when violated >0.

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