Method, device and equipment for predicting power generation of photovoltaic module under complex shading condition

Abstract

The disclosure provides a photovoltaic module power generation prediction method, device and equipment under complex shading conditions, by constructing a direction-resolved sky scattered radiation vector, optimizing the data structure and generation path of the radiation input, making each element in the direction-resolved sky scattered radiation vector correspond to a determined sky incident direction, which can be directly used as the boundary input for subsequent calculation; the sky radiation vector is coupled with three-dimensional shading, directional reflection, sub-cell unit double-sided light receiving state and actual series-parallel topology, and the current-voltage characteristic curves of the sub-cell unit, the cell, the sub-string, the photovoltaic module and the photovoltaic array are sequentially solved to solve the maximum output power and realize the prediction of the power generation; the photovoltaic louver and the facade BIPV are naturally in a complex non-uniform local shading scene, which leads to serious electrical mismatch and power attenuation in the cell, and reduces the power generation prediction error.

Description

Technical Field

[0001] This disclosure relates to the field of photovoltaic power generation technology, and in particular to methods, apparatus and equipment for predicting the power generation of photovoltaic modules under complex shading conditions. Background Technology

[0002] Building integrated photovoltaic (BIPV) systems have become an important form of photovoltaic application in recent years. Among them, adjustable photovoltaic louvers (PVB) and vertically installed facade BIPV modules combine the three core functions of building shading, building envelope, and clean power generation, and are compatible with the facade forms of most public and residential buildings, thus gaining widespread promotion and application.

[0003] Currently, bifacial photovoltaic (BIPV) and PVB systems commonly employ bifacial photovoltaic (PV) module technology, achieving a power generation gain of 10%-30% compared to monofacial modules. However, these facade PV systems are inherently situated in complex scenarios of uneven local shading: including dynamic mutual shading between PVB blades, fixed local shading from adjacent buildings / vegetation, corner shading from building components, and shadow shading from facade decorative lines. In this scenario, not only is the irradiance distribution on the front of the PV modules extremely uneven, but the reflected radiation received on the back of the modules also exhibits significant non-uniform distribution characteristics due to variations in the outline of shading objects, the position of the reflecting surface, and the blade angle. This non-uniform irradiance directly leads to severe electrical mismatch and power attenuation within, between, and between cells and modules, significantly increasing the error of current PV power generation prediction models and hindering the optimization of dynamic component attitude control, such as bifacial PV louvers. Summary of the Invention

[0004] The purpose of this disclosure is to provide a method, apparatus, and equipment for predicting the power generation of photovoltaic modules under complex shading conditions, which can solve one or more of the aforementioned problems in the prior art.

[0005] This disclosure addresses the aforementioned issues by proposing a method, apparatus, and equipment for predicting the power generation of photovoltaic modules under complex shading conditions.

[0006] To solve at least one of the above-mentioned technical problems, this disclosure proposes the following technical solution: Firstly, a method for predicting the power generation of photovoltaic modules under complex shading conditions is provided, including the following steps: Construct a direction-resolved sky scattering radiance vector, which includes the absolute scattering radiance of each sky patch; Each solar cell is divided into multiple smallest virtual space computing units, and the smallest virtual space computing unit is denoted as a sub-cell cell. Calculate the total irradiance on the front and back sides of each subcell, and correct the incident angle of light on the subcell. Electrical coupling is performed on the front and back sides of the subcell to obtain the current-voltage characteristic curve of a single subcell. The current-voltage characteristic curves of the subcell cells are coupled step by step to obtain the current-voltage characteristic curves of the target level. The maximum output power is determined based on the current-voltage characteristic curve of the target level, and the predicted power generation within the target time interval is calculated based on the maximum output power at different times.

[0007] Secondly, a photovoltaic module power generation prediction device under complex shading conditions is provided for executing any of the above-mentioned photovoltaic module power generation prediction methods under complex shading conditions. The device includes: The direction-resolved sky scattering radiance vector construction module is used to construct the direction-resolved sky scattering radiance vector, which includes the absolute scattering radiance of each sky patch. The sub-cell partitioning module is used to divide each cell into multiple minimum virtual space calculation units, and the minimum virtual space calculation unit is denoted as a sub-cell unit. The subcell irradiance calculation module is used to calculate the total front irradiance and total back irradiance of each subcell, and to correct the incident angle of light on the subcell. The subcell cell current-voltage characteristic curve calculation module is used to electrically couple the front and back sides of the subcell cell to obtain the current-voltage characteristic curve of a single subcell cell. The current-voltage characteristic curve coupling module is used to couple the current-voltage characteristic curves of the sub-cell cells step by step to obtain the current-voltage characteristic curves of the target level. The power generation prediction module is used to solve for the maximum output power based on the current-voltage characteristic curve of the target level, and to calculate the predicted power generation within the target time interval based on the maximum output power at different times.

[0008] Thirdly, a photovoltaic module power generation prediction device under complex shading conditions is provided. The device includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores at least one instruction, at least one program, code set, or instruction set. The at least one instruction, at least one program, code set, or instruction set is loaded by the processor and executed as described above in the photovoltaic module power generation prediction method under complex shading conditions of this disclosure.

[0009] Fourthly, a computer-readable storage medium is provided, which stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded by a processor and executed by a processor to provide a method for predicting the power generation of a photovoltaic module under complex shading conditions as described above.

[0010] The beneficial effects of this disclosure are that by constructing a direction-resolved sky scattering radiation vector, the data structure and generation path of the radiation input are optimized, so that each element in the direction-resolved sky scattering radiation vector corresponds to a determined sky incidence direction and can be directly used as the boundary input for subsequent calculations; by coupling the sky radiation vector with three-dimensional shading, directional reflection, the double-sided light-receiving state of the sub-cell unit and the actual series-parallel topology, the current-voltage characteristic curves of the sub-cell unit, cell, sub-string, photovoltaic module and photovoltaic array are solved in sequence to solve the maximum output power and realize the prediction of power generation; the problem of photovoltaic louvers and facade BIPV being in a complex non-uniform local shading scenario, which leads to severe electrical mismatch and power attenuation inside the cell, is solved, thus reducing the error in power generation prediction.

[0011] Furthermore, unless otherwise specified in this disclosure, all technical solutions can be implemented using conventional methods in the field. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart of a method for predicting the power generation of a photovoltaic module under complex shading conditions, provided as an embodiment of this disclosure.

[0014] Figure 2 This is a schematic diagram of the multi-level electrical structure in a photovoltaic module power generation prediction method under complex shading conditions provided in an embodiment of this disclosure.

[0015] Figure 3 This is a schematic diagram of the structure of a photovoltaic module power generation prediction device under complex shading conditions, provided in an embodiment of this disclosure. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0017] Example 1: Reference manual attached Figure 1 This invention illustrates a method for predicting the power generation of a photovoltaic module under complex shading conditions, according to an embodiment of the present disclosure, comprising the following steps: Step 1: Construct the direction-resolved sky scattering radiance vector, which includes the absolute scattering radiance of each sky patch; Step 2: Divide each solar cell into multiple smallest virtual space calculation units, and denote the smallest virtual space calculation unit as a sub-cell unit; Step 3: Calculate the total irradiance on the front and back sides of each subcell, and correct the incident angle of light on the subcell. Step 4: Electrically couple the front and back sides of the subcell to obtain the current-voltage characteristic curve of a single subcell. Step 5: Couple the current-voltage characteristic curves of the subcell cells step by step to obtain the current-voltage characteristic curves of the target level; Step 6: Based on the current-voltage characteristic curve of the target level, calculate the maximum output power, and calculate the predicted power generation within the target time interval based on the maximum output power at different times.

[0018] For each calculation time, the corresponding power-voltage characteristic is calculated based on the current-voltage characteristic curve of the target level, and the maximum output power is determined. Based on the maximum output power obtained from multiple calculation times, it is integrated or discretely accumulated over the target time interval to obtain the predicted power generation within the target time interval.

[0019] Therefore, the technical solution provided in this disclosure can calculate the total irradiance of the front and back of the subcell unit by constructing a direction-resolved sky scattering radiation vector, then perform bi-sided electrical coupling of the subcell unit, and finally integrate and output the power generation according to the actual electrical topology step by step.

[0020] In step 1, constructing the direction-resolved sky-scattered radiation vector may include the following steps: A series of low dynamic range (LDR) images are continuously acquired according to a preset exposure sequence; Several acquired LDR images are combined into a high dynamic range (HDR) image; Perform brightness calibration on the synthesized HDR image; The spatial distribution of the sky recorded in the HDR image is converted into a direction-resolved sky scattering radiation vector.

[0021] LDR image acquisition can be achieved using a fisheye lens or a wide-angle lens. Specifically, the image acquisition device equipped with a fisheye lens or a wide-angle lens is placed near the target photovoltaic module, so that the field of view of the fisheye lens or wide-angle lens can cover the sky hemisphere and the external environment facing the target photovoltaic module.

[0022] By pre-setting an exposure sequence, the image acquisition device can continuously capture multiple LDR images according to the pre-set exposure sequence, and then combine the continuously captured multiple LDR images into a single high dynamic range (HDR) image. In an optional embodiment, combining multiple LDR images into a single HDR image can be achieved using existing HDR image synthesis methods.

[0023] Therefore, synthesized HDR images can extract the relative radiance distribution of each sky segment and information about the sun's direction.

[0024] Brightness calibration of the synthesized HDR image may include the following steps: A vertical illuminance sensor is installed in front of the fisheye or wide-angle lens of the image acquisition module. The vertical illuminance sensor is used to collect the vertical illuminance value in front of the lens. , Extract the luminance value of each pixel from the synthesized HDR image. And calculate the vertical illuminance value of the HDR image. The calculation formula is shown in formula (1).

[0025] In the formula, This represents the vertical illuminance value of an HDR image, measured in lx. This represents the brightness value of the i-th pixel, where i is a natural number and the unit is cd / m². 2 , This represents the angle of incidence of the i-th pixel. Represents the solid angle of the i-th pixel; like If so, the synthesized HDR image is determined to require no brightness calibration; like This indicates that there is a possibility of saturation or overflow in the bright areas of the sun in the HDR image, and it is determined that the synthesized HDR image needs brightness calibration to compensate for the energy loss in the bright areas of the HDR image. If the synthesized HDR image requires brightness calibration, then the measured vertical illuminance value should be used. And the required brightness value of the pixel is calculated using formula (1). Soon Substituting into the left side of formula (1), the calculated brightness value is denoted as: , Based on the calculated brightness value of the pixel Adjust the HDR image to complete the brightness calibration. The adjusted image should meet the requirements of formula (2):

[0026] In the formula, This represents the vertical illuminance value of the HDR image after brightness calibration. The measured vertical illuminance value is used to calculate the visible light irradiance value corresponding to the HDR image using equation (3). :

[0027] In the formula, The unit is W / m 2 a and b are the total radiation in the visible light band and the vertical illuminance calculated from the HDR image. a and b can be obtained through measurement.

[0028] Therefore, without introducing additional HDR images, physical calibration of HDR images can be achieved through post-image synthesis processing, thereby establishing a mapping relationship between pixel brightness values ​​and visible light irradiance. This enables the acquisition of directional image information of the sky and external environment at the current moment, providing a basis for the relative spatial distribution and radiometric calibration of subsequent sky patch radiance vectors.

[0029] Converting the spatial distribution of the sky recorded in an HDR image into a direction-resolved sky scattering radiance vector may include the following steps: Identify sky regions and discretize the sky hemisphere into several sky segments. Calculate the relative visible light scattering radiance for each sky patch; A reference surface is set up, and the relative scattering radiance of visible light in each sky segment is normalized according to the scattering radiance of the reference surface to obtain the absolute scattering radiance of each sky segment. The sky patch scattering radiance vector includes the absolute scattering radiance of all sky patches.

[0030] When the lens in the image acquisition device is a fisheye lens, it is necessary to identify the sky region based on fisheye geometric mapping and sky masking. Both fisheye lens mapping and sky masking identification can be implemented using conventional algorithms known in the art, and their specific steps are not the focus of this application and will not be elaborated here.

[0031] Discretizing the sky hemisphere into several sky segments can be done by dividing the sky hemisphere into N sky segments with defined center directions, elevation angles, and solid angles. N can be selected according to the calculation accuracy. For example, the Tregenza sky hemisphere subdivision method can be used to divide the entire sky hemisphere into 145 sky segments with specific center directions, elevation angles, and solid angles, in which case N is 145.

[0032] Calculating the visible light relative scattering radiance of each sky patch can include: statistically analyzing the effective sky pixels within each sky patch to obtain the visible light relative scattering radiance of the k-th sky patch at time t, and identifying the solar disk region, cloud region, clear sky background, horizon region, and occlusion region. The aforementioned methods for calculating visible light relative scattering radiance and for region identification can all be implemented using methods already disclosed in the prior art, and will not be elaborated upon here. For example, the red-blue ratio thresholding method can be used to segment cloud regions from a clear sky background; the color distribution variance minimization method can be used to detect the horizon position in the image to determine the region near the horizon; and the solar disk position can be identified through the bright saturated regions in the image.

[0033] In setting a reference surface and normalizing the relative visible light irradiance of each sky patch based on the reference surface's irradiance to obtain the absolute irradiance of each sky patch, the reference surface's irradiance can be obtained through measurement or inversion. For example, when the reference surface is set to a horizontal plane, the preferred reference quantity is DHI (Diffuse Horizontal Irradiance), which can be measured using a irradiance sensor. The normalization relationship can then be expressed as:

[0034] In the formula, Let represent the absolute scattered radiance of the k-th sky patch at time t, where k is a natural number not less than 1 and not greater than N. Indicates the irradiance scattered across the reference surface. This represents the relative radiance of visible light scattered across the k-th sky segment at time t; Let be the angle between the direction of the j-th sky segment and the normal to the reference plane; Let j be the solid angle of the j-th sky segment. This represents the direction-resolved sky-scattered radiation vector.

[0035] The visible light relative scattered radiance of each sky segment is normalized by the reference surface irradiance to obtain the radiance ratio of each sky segment. Then, the measured DHI is decomposed into absolute scattered radiance of different sky segments using the radiance ratio of each sky segment. The wavelength corresponding to the absolute scattered radiance of each sky segment is determined by the measured DHI.

[0036] In an optional embodiment, the direction-resolved sky scattering radiation vector and the direction, solid angle, timestamp, and band information of each sky patch can be organized into a data structure that can be used for subsequent calculations. This data structure can be in vector, matrix, SMX file, or other equivalent format, and is not limited to any specific software or file type.

[0037] Therefore, instead of compressing the sky spatial information in the HDR image into the total irradiance of a single horizontal plane or a small amount of direct and scattered scalars before reconstructing the sky distribution, the directional radiation information of different sky slices is extracted from the HDR image at the current moment to form a direction-resolved sky scattering radiation vector that is updated with each sampling moment. Each element in this vector corresponds to a determined sky incident direction and is directly used as the boundary input for subsequent sub-cell irradiance calculation.

[0038] In step 2, each cell is divided into multiple minimum virtual space calculation units, which are denoted as sub-cell cells.

[0039] Specifically, a single, complete solar cell is discretized into a uniform grid to obtain sub-cell units. The solar cell can be a crystalline silicon photovoltaic cell. The sub-cell unit is the smallest independent unit capable of irradiance mapping, temperature correction, and electrical characteristic calculation in the photovoltaic module power generation prediction method under complex shading conditions provided in this disclosure. At the numerical simulation level, the smaller unit division of the solar cell allows for power generation prediction without any physical structural modifications to the solar cell, lowering the implementation threshold and cost of the photovoltaic module power generation prediction method under complex shading conditions. This allows it to be applied to existing photovoltaic modules or arrays, expanding its applicability and ensuring stable solar cell performance.

[0040] In an optional embodiment, a single complete solar cell can be uniformly meshed and discretized to obtain M×N sub-cell units.

[0041] In an optional embodiment, the solar cell can be divided into multiple sub-cell units, either uniformly or non-uniformly, based on the cell's geometry, local irradiation gradient, grid line orientation, and computational accuracy requirements.

[0042] It should be understood that there is no strict temporal order between steps 1 and 2. They can be executed in any order or in parallel. This application does not impose any restrictions on this.

[0043] In step 3, calculating the total front irradiance and total back irradiance of each subcell and correcting the incident angle of light onto the subcell may include the following steps: Calculate the front sky diffuse irradiance and back sky diffuse irradiance for each subcell cell; Calculate the direct solar irradiance on the front and back sides of each subcell; Calculate the reflected irradiance from the front and back of each subcell. The total irradiance of the front of each subcell is equal to the sum of the front sky diffuse irradiance, the front direct solar irradiance, and the front irradiance reflected by obstructions. The total back irradiance of each subcell is equal to the sum of the back sky diffuse irradiance, the back direct solar irradiance, and the back irradiance reflected by the obstruction.

[0044] The calculation of the front sky diffuse irradiance and back sky diffuse irradiance of the subcellular cell includes the following steps: The direction-resolved sky scattering radiance vector constructed in step 1 is discretized into N sky patches with defined center directions and solid angles. N can take the value 145. Then, for the q-th subcell cell and the k-th sky patch, the contribution of the sky patch to the scattered irradiance generated by the subcell cell is:

[0045] The sky-scattered irradiance received by the s-th side receiving surface of the q-th sub-cell is:

[0046] In the formula, The f-th receiving surface and the r-th receiving surface represent the front and back sides of the subcell cell, respectively. The scattered radiance of the k-th sky patch is expressed in W / (m²·sr); Let be the solid angle of the k-th sky segment, in units of sr; The direction of the center of the k-th sky segment is related to the direction of the k-th sky segment. The angle of incidence between the out-of-plane normal vectors of the s-th side receiver of each subcell cell; This is the correction factor for the incident angle in the corresponding incident direction; The occlusion coefficient is the shading coefficient of the k-th sky segment direction relative to the s-th side receiving surface of the q-th subcell.

[0047] In an optional embodiment, the front of the subcell cell is defined as the side of the subcell cell that directly faces the sun or the side of the subcell cell whose normal and the sun vector are at an angle between [-90°, 90°].

[0048] When there is no obstruction in the direction from the s-th side receiving surface of the q-th sub-cell to the k-th sky segment. When this direction is completely blocked by an opaque component, When the subcell cell is only partially blocked or light passes through the light-transmitting component, It can be represented as:

[0049] In the formula, This represents the area of ​​the front or back side of the q-th subcell. The front and back areas of a single subcell are equal, and the unit is m. 2 ; The unobstructed area is the region projected from the s-th side receiving surface of the q-th subcell to the k-th sky segment, in meters. 2 ; The area of ​​the projected region through which light passes through the translucent component, expressed in m². 2 ; Let be the effective transmittance of the light-transmitting component in the direction from the receiving surface on the s-th side of the q-th subcell cell to the k-th sky segment.

[0050] Calculating the front and back direct solar irradiance of each subcell may include the following steps: Direct solar radiation is an independent directional input with a defined solar orientation. DNI (Direct Normal Irradiance) can be obtained through a direct normal irradiance sensor, a solar-tracking direct radiometer, or a reference photovoltaic cell. When the solar disk region in an HDR image is not saturated and has undergone absolute radiometric calibration, the direct normal irradiance can also be obtained by integrating the radiance of the solar disk region.

[0051] For the s-th receiving surface of the q-th subcell, the received direct solar irradiance is:

[0052] in, , representing the front and back sides of the subcell cell, respectively; The normal direct irradiance at time t, in units of ; is the angle between the solar incident direction and the out-of-plane normal vector of the s-th side receiving surface of the q-th subcell; This is the correction factor for the angle of incidence in the direction of solar incidence; The shading coefficient is the shading factor of the sun relative to the front or back of the subcell.

[0053] When the path of direct sunlight involves only opaque shading, the shading coefficient can be expressed as:

[0054] In the formula, Let q be the total area of ​​the q-th subcell; The area of ​​the obstruction formed by the opaque component on the s-th side receiving surface of the q-th subcell cell in the direction of sunlight.

[0055] When direct sunlight can pass through glass or other light-transmitting components, the shading factor can be further expressed as:

[0056] In the formula, The area of ​​the open region projected in the direction of the sun; The area of ​​the projected region of sunlight passing through the light-transmitting component; The effective transmittance of the light-transmitting component corresponding to the direction of solar incidence is used. The open area, the area blocked by the light-transmitting component, and the area blocked by the opaque component are combined to cover the q-th subcell cell, avoiding overlapping areas from being counted repeatedly. Here, the open area refers to the area on the subcell cell that is completely unblocked.

[0057] The formula for calculating the incident angle correction factor is as follows:

[0058]

[0059]

[0060] In the formula, The angle of incidence is The average reflectivity of the photovoltaic module cover plate; The reflectivity of the photovoltaic module cover plate at an incident angle of 0°; Approximately 1.0, which is the refractive index of air; The refractive index of the photovoltaic module cover plate changes with the material of the cover plate. When the photovoltaic module cover plate is made of glass, The value is 1.5; The angle of incidence is expressed in rad. In direct radiation calculations, it represents the angle of incidence for direct sunlight; in diffuse radiation calculations, it represents the angle of incidence for each sky segment direction vector. The angle of refraction represents the angle between the refracted ray and the normal to the surface of the photovoltaic module cover after the direct solar radiation, sky diffuse radiation, or radiation reflected by the obstruction enters the photovoltaic module cover from the air. The angle of refraction can be calculated according to Snell's law, i.e., formula (13), and the unit is rad.

[0061] Since solar cells cannot be directly exposed to the outside environment, a photovoltaic (PV) module cover is an essential structure that protects the cells. Common PV module cover materials include glass and other light-concentrating materials. Through the calculations above, considering both direct and diffuse incident light, the reflection loss of the PV module cover is calculated, and the incident angle of the incident light is accurately determined. This allows for the calculation of how much light actually reaches the surface of the solar cells after passing through the cover, significantly improving the accuracy of PV module power generation prediction.

[0062] When calculating the front and back irradiance of each sub-cell, the complete radiation transmission path is taken as direct sunlight and sky scattering—ground, building structure, or adjacent photovoltaic structure—sub-cell. This quantifies the directional reflected radiation contributions of external shading objects and the ground to the front and back of the sub-cell, solving the technical problems of traditional models failing to describe the anisotropic characteristics of reflected radiation under non-uniform shading and the inaccurate calculation of backsheet reflection in bifacial modules. The front and back irradiance calculations not only directly treat the ground as a shading object for radiation value calculation but also calculate the reflected irradiance of other shading objects. Therefore, both the front and back irradiance calculations include ground reflected radiation and shading object reflected radiation.

[0063] Calculating the front and back irradiance of each subcell can include the following steps: For the The first sub-cell The side receiving surface is further divided into There are several receiving sub-regions, and a discrete receiving point is set up in each receiving sub-region. The f-th receiving surface represents the front side of the sub-cell cell, and the r-th receiving surface represents the back side of the sub-cell cell; j represents the j-th discrete receiving point. .

[0064] The discrete receiving points are used for calculating the reflected radiation from the obstruction and do not specifically refer to the four vertices of the subcellular cell.

[0065] In an optional embodiment, any receiving surface of the subcell cell can be divided into regular or irregular receiving sub-regions, with the center point of each receiving sub-region as a discrete receiving point and the area of ​​the corresponding receiving sub-region as the area weight of that receiving point.

[0066] Transmit from each discrete receiving point to the outer hemispherical space of its corresponding receiving surface. There are i hemispherical sampling rays, where i represents the i-th hemispherical sampling ray. .

[0067] When the i-th hemispherical sampling ray first hits the ground, building components, adjacent photovoltaic components, or other obstructions, the corresponding obstruction intersection point is obtained. Then, starting from this obstruction intersection point, visibility tracing is performed along the representative directions of each sky segment to determine the radiation contribution of each sky segment to this obstruction intersection point. The number of hemispherical sampling rays emitted from a discrete receiving point to the outer hemispherical space is determined based on the hemispherical sampling angle resolution and calculation accuracy, and is not limited to a specific value. The number of sky slices.

[0068] This embodiment uses the Tregenza sky hemisphere division method, therefore:

[0069] The reflected irradiance from the obstruction at the j-th discrete receiving point on the s-th side receiving surface of the q-th sub-cell cell is expressed as:

[0070] In the formula, This indicates whether the i-th hemispherical sampling ray emitted from the j-th discrete receiving point on the s-th side of the q-th subcell cell first hits the obstruction. Its value is 1 if it hits and 0 otherwise. This represents the result of visibility tracking along the center direction of the k-th sky segment, starting from the intersection of the obstruction hit by the i-th hemispherical sampling ray emitted from the j-th receiving point on the s-th side of the q-th subcell cell. Let be the solid angle weight of the directional region represented by the i-th hemispherical sampling ray; Let represent the radiance and solid angle of the k-th sky segment, respectively; Let be the bidirectional reflection distribution function of the surface of the obstruction, which can describe the incident direction from the sky. To the direction of photovoltaic emission Energy distribution; Let be the unit vector pointing from the intersection of the occlusion points determined by the i-th hemispherical sampling ray to the center of the k-th sky patch; vector and Let represent the outer normal vectors of the surface of the obstruction and the receiving surface of the sub-cell cell, respectively. This represents the cosine correction factor for the radiation incident on the surface of the obstructing object in the k-th sky patch. The cosine correction factor represents the incident radiation on the receiving surface of the subcellular cell when the obstruction reflects radiation. Here is the transmittance correction term based on Fresnel's law at the incident angle, where The angle between the reflected ray and the normal vector of the receiving surface of the subcell cell is denoted as .

[0071] The total irradiance on the front of each subcell is equal to the sum of the irradiance scattered from the sky on the front, the direct irradiance from the sun on the front, and the irradiance reflected by the obstructions on the front. The total irradiance on the back of each subcell is equal to the sum of the irradiance scattered from the sky on the back, the direct irradiance from the sun on the back, and the irradiance reflected by the obstructions on the back. The expressions are as follows:

[0072] In the formula, f represents the front side of the subcell cell, and r represents the back side of the subcell cell.

[0073] When calculating the reflected irradiance of the front shading object of the subcell cell, the reflected radiation from the ground, distant buildings and / or vegetation is traced. The ray tracing range covers the entire forward hemisphere space, and the maximum number of reflection tracing times can be set to 1-3 times.

[0074] When calculating the reflected irradiance of the back cover of the sub-cell, the near-distance reflected radiation of adjacent photovoltaic blades, building facades, and window frame components is tracked. The ray tracing range is the limited space between the back of the module and the building facade. The maximum number of reflection tracing times can be set to 1-5 times, which can accurately capture multiple reflected radiations between blades and components, solving the problem of inaccurate back panel reflection calculation in traditional models.

[0075] In an optional embodiment, for each subcell, the operating temperature of each subcell can be calculated using the Sandia empirical temperature model based on the total irradiance on the front and back of the subcell, the ambient temperature, and the wind speed parameters, replacing the traditional average temperature calculation of the entire component and improving the accuracy of the calculation.

[0076] Ambient temperature and wind speed parameters can be collected using ambient temperature sensors, backsheet temperature sensors, and wind speed sensors. Ambient temperature and wind speed sensors can obtain the environmental parameters required for the photovoltaic module's operating temperature or temperature model.

[0077] In an optional embodiment, when no independent DHI or DNI measurement module is set, a total irradiance sensor can be set. The total irradiance sensor can acquire the GHI or the total irradiance of the target mounting surface and obtain the corresponding scattered or direct radiation input through a radiometric decomposition method.

[0078] In step 4, electrically coupling the front and back sides of the subcell to obtain the current-voltage characteristic curve of a single subcell may include the following steps: For each subcell cell, an electrical calculation sub-model is established on the front and back sides respectively. The total irradiance on the front and back sides of the subcell cell is used as input to obtain the current-voltage characteristic curves of the front power generation module and the back power generation module respectively. By integrating the front and rear power generation modules of the sub-cell in a parallel structure, the current-voltage characteristic curve of a single sub-cell is obtained.

[0079] The electrical calculation model can be a single-diode five-parameter model. That is, for each sub-cell with a clear spatial location attribute, the total irradiance on the front and back sides of the sub-cell location and the operating temperature of the sub-cell are used as inputs to establish single-diode five-parameter models for the front and back power generation modules, respectively. Based on the condition of consistent terminal voltage, the electrical coupling of the front and back sides of the sub-cell at the same spatial location is completed to form the current-voltage characteristic curve of a single sub-cell. The current-voltage characteristic curve of a single sub-cell is then corrected to obtain the current-voltage characteristic curve under actual operating conditions.

[0080] For the front and rear power generation modules of each sub-cell, based on its total irradiance and operating temperature, the IV characteristics are described using a single diode five-parameter model. The current-voltage relationship expression is as follows:

[0081] In the formula, Represents photocurrent, measured in amperes (A), which varies with the total irradiance (G) on the front or back of the subcell and the operating temperature. Approximately linear change; This represents the reverse saturation current, measured in amperes (A). This indicates series resistance, with the unit being Ω; This indicates parallel resistance, with the unit being Ω; V t It is the equivalent thermal voltage corresponding to the diode's ideality factor. q is the diode ideality factor, and q is the electron charge; is the Boltzmann constant.

[0082] Since the terminal voltages of the front and rear power generation modules of a single sub-cell are the same, a parallel structure can be used to integrate the power generation outputs of the front and rear of a single sub-cell, resulting in the complete current-voltage characteristic curve of a single sub-cell. The expression for the current-voltage characteristic curve of a single sub-cell is as follows:

[0083] In the formula, the subscript f represents the front power generation module of the sub-cell, the subscript r represents the back power generation module of the sub-cell, and the superscript c represents the integrated parameters of a single sub-cell. This represents the total photocurrent generated after the bi-sided integration of the sub-cell cells, expressed in amperes (A). This represents the series resistance of the integrated bi-sided sub-cell cell, expressed in Ω. This represents the parallel resistance of the sub-cell after bifacial integration, in Ω. Additionally, it represents the reverse saturation current I0 and equivalent thermal voltage V of the sub-cell's back-side power generation module. t Consistent with the front-side power generation module of the sub-cell cell.

[0084] The current-voltage characteristic curve of a single subcell cell is corrected based on the total irradiance and operating temperature of the front and back sides of the subcell cell to obtain the current-voltage characteristic curve of the q-th subcell cell under actual operating conditions, as expressed below:

[0085] In the formula, It is the band gap energy. It is the elementary charge. This is the diode ideality factor, and k is the Boltzmann constant. and These represent the irradiance and temperature under standard test conditions, respectively, and are 1000 W / m². 2 and 25℃, and It is the temperature coefficient of the short-circuit current.

[0086] For photovoltaic modules or photovoltaic arrays, a multi-level electrical structure may include: electrically coupling several sub-cell units with different spatial location attributes within the same cell according to the equivalent parallel connection relationship within the cell; connecting several cells in series to form sub-strings; connecting several sub-strings in series, parallel, or series-parallel to form a photovoltaic module; and connecting several photovoltaic modules in series, parallel, or series-parallel to form a photovoltaic array.

[0087] In step 5, the target level can be a photovoltaic module or a photovoltaic array.

[0088] When the target level is a photovoltaic module, the current-voltage characteristic curve of the sub-cell is coupled step by step to obtain the current-voltage characteristic curve of the target level, which may include: The current-voltage characteristic curve of a single solar cell is obtained by calculating all sub-cells within the same solar cell according to the parallel connection rule. The solar cells are connected in series to form sub-strings. The sub-strings are then calculated according to the actual series-parallel relationship to obtain the current-voltage characteristic curve of the photovoltaic module.

[0089] When the target level is a photovoltaic array, the current-voltage characteristic curves of the sub-cell cells are coupled step by step to obtain the current-voltage characteristic curves of the target level, which may include: The current-voltage characteristic curve of a single solar cell is obtained by calculating all sub-cells within the same solar cell according to the parallel connection rule. The solar cells are connected in series to form sub-strings. The sub-strings are then calculated according to the actual series-parallel relationship to obtain the current-voltage characteristic curve of the photovoltaic module. The photovoltaic modules are electrically coupled according to the actual series-parallel relationship to obtain the current-voltage characteristic curve of the photovoltaic array.

[0090] When performing calculations at the cell level, all subcells within the same cell are connected in parallel, following the parallel circuit rule of identical terminal voltages and summed currents. The complete IV characteristic curve of a single cell can be obtained using the voltage scanning method. This allows for the quantification of electrical mismatch losses between subcells within a cell.

[0091] When performing calculations at the substring level, the substring is composed of... The cells are connected in series, following the series circuit rules that the current is the same and the terminal voltage is the sum of the voltages of each cell.

[0092] Based on substring current With the photocurrent of the solar cell Based on their relative sizes, the solar cells are divided into two operating states, and their terminal voltage V is calculated for each state. c :

[0093] To prevent excessive reverse bias and hot spots caused by shading in local cells, a bypass diode with opposite polarity is connected in parallel across each substring. When the total terminal voltage of the substring is lower than the forward voltage drop of the bypass diode, the diode conducts, and the substring is bypassed.

[0094] In the formula, The output voltage of the component substring is given when the current is I, in volts (V). N represents the terminal voltage of the m-th solar cell when the current is I, in V. c This represents the total number of cells connected in series within a component substring, and is dimensionless. The forward voltage drop of the bypass diode is 0.7V for silicon-based diodes.

[0095] When integrating photovoltaic (PV) modules, the integration needs to be based on the actual series and parallel connection methods of each sub-string within the PV module. Similarly, when integrating PV arrays, the integration needs to be based on the actual series and parallel connection methods of each PV module within the PV array.

[0096] Taking the photovoltaic module level as the target level as an example, assuming that the photovoltaic module consists of... The photovoltaic module is composed of several substrings connected in series. The complete IV curve is solved using the current scanning method. The terminal voltage of the photovoltaic module is the sum of the terminal voltages of all substrings.

[0097] In the formula, V represents the output voltage of the photovoltaic module when the current is I. The output voltage of the k-th substring is given by current I, in volts (V). The total number of substrings connected in series within the photovoltaic module is dimensionless; P is the output power of the photovoltaic module when the current is I, in W.

[0098] Therefore, it is possible to output the I-V curve of a photovoltaic module or photovoltaic array.

[0099] When the target level is a photovoltaic array, the photovoltaic modules are electrically coupled according to their actual series, parallel, or series-parallel connections to obtain the current-voltage characteristic curve of the photovoltaic array. Taking a series connection of photovoltaic modules as an example, under the same scanning current I, the terminal voltage of the photovoltaic array is equal to the sum of the terminal voltages of each photovoltaic module:

[0100] In the formula, The output voltage of the photovoltaic array under current I is expressed in volts (V). Let be the output voltage of the u-th photovoltaic module under current I, in V; The total number of photovoltaic modules connected in series in the photovoltaic array is dimensionless.

[0101] If the components are connected in parallel, the following should be written separately:

[0102] In the formula, The output current of the photovoltaic array under voltage V; Let be the output current of the u-th photovoltaic module under voltage V. For photovoltaic arrays with a hybrid series-parallel connection, coupling is performed step by step according to the rule of adding the voltages of each series branch and adding the currents of each parallel branch.

[0103] Step 6: Based on the current-voltage characteristic curve of the target level, obtain the predicted power generation, where the expression for the predicted power is:

[0104] In the formula, tar represents the target level; when the target level is a photovoltaic module, When the target level is a photovoltaic array, .

[0105] Based on the predicted power, the power generation is calculated, and the power generation is equal to the integral of the predicted power over the time interval.

[0106] The technical solution provided in this disclosure generates a direction-resolved sky scattering radiation vector from the HDR image at the current moment, and enables each sky slice to independently participate in the sub-cell irradiance calculation. It optimizes the data structure and generation path of the radiation input, so that each element in the direction-resolved sky scattering radiation vector corresponds to a determined sky incident direction, which can be directly used as the boundary input for subsequent calculations. The sky radiation vector is coupled with three-dimensional shading, directional reflection, the double-sided light-receiving state of the sub-cell, and the actual series-parallel topology. The current-voltage characteristic curves of the sub-cell, cell, sub-string, photovoltaic module, and photovoltaic array are solved sequentially to solve for the maximum output power and realize the prediction of power generation.

[0107] Compared with the prior art, the technical solution provided in this disclosure has the following advantages: (1) Fully consider the anisotropic characteristics of actual sky brightness, the spatial cutoff effect of local shading on the radiation field, and the non-uniform distribution of reflected radiation from the ground / shading objects. Especially in the scenario of uneven local shading on the facade, restore the real spatial distribution of radiation and reduce calculation errors. (2) The IAM effect of scattered radiation is corrected in one direction to further improve the calculation accuracy. Based on the ray path projection tracking of sky segments, the incident angle correction of anisotropic scattered radiation is realized through Fresnel's law, which overcomes the defect of the traditional model using a fixed average correction coefficient for scattered radiation and perfectly adapts to the difference in incident angle of scattered light from different sky segments in the facade scene. (3) The accuracy of power generation calculation has been improved in a hierarchical manner. By setting up the sub-cell level model, the non-uniform distribution of irradiance inside the photovoltaic module and the cell, as well as the resulting electrical mismatch loss, are fully considered, which greatly improves the prediction accuracy. It has a high degree of adaptability to the high-frequency local shading scenarios of photovoltaic louvers and facade BIPV. (4) The photocurrent contribution of the front and back sides of the subcell is coupled to preserve the spatial correspondence of the irradiation distribution on both sides of the subcell. (5) Establish an actual topology transfer mechanism from subcell cells, cells, substrings, photovoltaic modules to photovoltaic arrays, which can be electrically integrated step by step according to subcell cells, cells, substrings, photovoltaic modules and photovoltaic arrays; (6) The measured direction of the sky, the bifacial response of the sub-cell unit and the actual electrical topology are unified so that the differences caused by the spatial distribution of the sky and local shading can be continuously transmitted to the photovoltaic module or the Guangfu array connection power, thereby improving the accuracy and applicability of power generation prediction calculation.

[0108] Example 2: This disclosure also provides a photovoltaic module power generation prediction device under complex shading conditions, used to execute any of the aforementioned photovoltaic module power generation prediction methods under complex shading conditions, including, The direction-resolved sky scattering radiance vector construction module 111 is used to construct the direction-resolved sky scattering radiance vector, which includes the absolute scattering radiance of each sky patch. Sub-cell unit partitioning module 112 is used to divide each cell into multiple minimum virtual space calculation units, and the minimum virtual space calculation unit is denoted as sub-cell unit. The subcell irradiance calculation module 113 is used to calculate the total front irradiance and total back irradiance of each subcell, and to correct the incident angle of light incident on the subcell. The subcell cell current-voltage characteristic curve calculation module 114 is used to electrically couple the front and back sides of the subcell cell to obtain the current-voltage characteristic curve of a single subcell cell. The current-voltage characteristic curve coupling module 115 is used to couple the current-voltage characteristic curves of the sub-cell cells step by step to obtain the current-voltage characteristic curves of the target level. The power generation prediction module 116 is used to obtain the predicted power generation based on the current-voltage characteristic curve of the target level.

[0109] The beneficial effects of this disclosure are that by constructing a direction-resolved sky scattering radiation vector, the data structure and generation path of the radiation input are optimized, so that each element in the direction-resolved sky scattering radiation vector corresponds to a determined sky incidence direction and can be directly used as the boundary input for subsequent calculations; by coupling the sky radiation vector with three-dimensional shading, directional reflection, the double-sided light-receiving state of the sub-cell unit and the actual series-parallel topology, the current-voltage characteristic curves of the sub-cell unit, cell, sub-string, photovoltaic module and photovoltaic array are solved in sequence to solve the maximum output power and realize the prediction of power generation; the problem of photovoltaic louvers and facade BIPV being in a complex non-uniform local shading scenario, resulting in severe electrical mismatch and power attenuation inside the cell, is solved, thus improving the accuracy of power generation prediction under complex shading conditions.

[0110] The apparatus provided in the above embodiments is only illustrated by the division of the above functional modules when implementing its functions. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the apparatus can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0111] Example 3: This disclosure provides a photovoltaic module power generation prediction device under complex shading conditions, the device comprising: One or more processors and memory.

[0112] The device may also include input devices and output devices.

[0113] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the photovoltaic module power generation prediction method under complex shading conditions in the embodiments of this disclosure. The processor executes various functional applications and data processing of the device by running the non-volatile software programs, instructions, and modules stored in the memory, thereby realizing the photovoltaic module power generation prediction method under complex shading conditions described in the above method embodiments.

[0114] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function. The data storage area may store data created based on the use of the photovoltaic module power generation prediction device under complex shading conditions. Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the photovoltaic module power generation prediction device under complex shading conditions via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0115] The input device can receive input digital or character information, and generate signal inputs related to user settings and function control. The output device may include display devices such as a display screen.

[0116] One or more modules are stored in memory and, when executed by one or more processors, perform the photovoltaic module power generation prediction method under complex shading conditions in any of the above method embodiments.

[0117] The photovoltaic module power generation prediction equipment under the aforementioned complex shading conditions can execute the method provided in the embodiments of this disclosure, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of this disclosure.

[0118] Example 4: On the other hand, Embodiment 4 of this disclosure provides a computer-readable storage medium storing one or more programs including execution instructions. The execution instructions can be read and executed by a device (including but not limited to a computer, server, or network device) to perform the relevant steps in the above method embodiments.

[0119] The embodiments described above are merely illustrative. The units or modules described as separate components may or may not be physically separate. Components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0120] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the prior art, can be expressed in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A method for predicting the power generation of photovoltaic modules under complex shading conditions, characterized in that, Includes the following steps: Construct a direction-resolved sky scattering radiance vector, wherein the direction-resolved sky scattering radiance vector includes the absolute scattering radiance of each sky patch; Each solar cell is divided into multiple smallest virtual space computing units, and the smallest virtual space computing unit is denoted as a sub-cell cell. Calculate the total irradiance on the front and back sides of each subcell, and correct the incident angle of light on the subcell. Electrical coupling is performed on the front and back sides of the subcell to obtain the current-voltage characteristic curve of a single subcell. The current-voltage characteristic curves of the subcell cells are coupled step by step to obtain the current-voltage characteristic curves of the target level. The maximum output power is determined based on the current-voltage characteristic curve of the target level, and the predicted power generation within the target time interval is calculated based on the maximum output power at different times.

2. The method for predicting photovoltaic module power generation under complex shading conditions according to claim 1, characterized in that, The constructed direction-resolved sky scattering radiation vector includes: Several LDR images are continuously acquired according to a preset exposure sequence; Several acquired LDR images are combined into an HDR image; Perform brightness calibration on the synthesized HDR image; The spatial distribution of the sky recorded in the HDR image is converted into a direction-resolved sky scattering radiation vector.

3. The method for predicting photovoltaic module power generation under complex shading conditions according to claim 2, characterized in that, The process of converting the spatial distribution of the sky recorded in the HDR image into a direction-resolved sky scattering radiation vector includes: Identify sky regions and discretize the sky hemisphere into several sky segments. Calculate the relative visible light scattering radiance for each sky patch; A reference surface is set up, and the relative scattering radiance of visible light in each sky segment is normalized according to the scattering radiance of the reference surface to obtain the absolute scattering radiance of each sky segment. The sky patch scattering radiance vector includes the absolute scattering radiance of all sky patches.

4. The method for predicting photovoltaic module power generation under complex shading conditions according to claim 1, characterized in that, The calculation of the total front irradiance and total back irradiance of each subcell includes: Calculate the front sky diffuse irradiance and back sky diffuse irradiance for each subcell cell; Calculate the direct solar irradiance on the front and back sides of each subcell; Calculate the reflected irradiance from the front and back of each subcell. The total irradiance of the front of each subcell is equal to the sum of the front sky diffuse irradiance, the front direct solar irradiance, and the front irradiance reflected by obstructions. The total back irradiance of each subcell is equal to the sum of the back sky diffuse irradiance, the back direct solar irradiance, and the back irradiance reflected by the obstruction.

5. The method for predicting photovoltaic module power generation under complex shading conditions according to claim 1, characterized in that, The electrical coupling of the front and back sides of the sub-cell to obtain the current-voltage characteristic curve of a single sub-cell includes: Electrical calculation models were established for the front and back sides of the sub-cell cell, respectively. The current-voltage characteristic curves of the front and back power generation modules were obtained. The contributions of the front and back sides to the photogenerated current were calculated. Based on the condition of consistent terminal voltage, an equivalent current-voltage model under the condition of double-sided light exposure was established.

6. The method for predicting photovoltaic module power generation under complex shading conditions according to claim 5, characterized in that, The current-voltage characteristic curve of a single subcell is corrected to obtain the current-voltage characteristic curve under actual operating conditions.

7. The method for predicting photovoltaic module power generation under complex shading conditions according to claim 1, characterized in that, Several sub-cell units are connected in parallel to form a solar cell, several solar cells are connected in series to form a sub-string, several sub-strings are connected to form a photovoltaic module, and several photovoltaic modules are connected to form a photovoltaic array. When the target level is a photovoltaic module, the step-by-step coupling of the current-voltage characteristic curves of the sub-cell cells to obtain the current-voltage characteristic curve of the target level includes: The current-voltage characteristic curve of a single solar cell is obtained by calculating all sub-cells within the same solar cell according to the parallel connection rule. The solar cells are connected in series to form sub-strings. The sub-strings are then calculated according to the actual series-parallel relationship to obtain the current-voltage characteristic curve of the photovoltaic module. When the target level is a photovoltaic array, the step-by-step coupling of the current-voltage characteristic curves of the sub-cell cells to obtain the current-voltage characteristic curves of the target level includes: The current-voltage characteristic curve of a single solar cell is obtained by calculating all sub-cells within the same solar cell according to the parallel connection rule. The solar cells are connected in series to form sub-strings. The sub-strings are then calculated according to the actual series-parallel relationship to obtain the current-voltage characteristic curve of the photovoltaic module. The photovoltaic modules are electrically coupled according to the actual series-parallel relationship, and the current-voltage characteristic curve of the photovoltaic array is obtained by solving.

8. A photovoltaic module power generation prediction device under complex shading conditions, used to execute the photovoltaic module power generation prediction method under complex shading conditions as described in any one of claims 1-7, characterized in that, include: A direction-resolved sky scattering radiance vector construction module is used to construct a direction-resolved sky scattering radiance vector, wherein the direction-resolved sky scattering radiance vector includes the absolute scattering radiance of each sky patch; The sub-cell partitioning module is used to divide each cell into multiple minimum virtual space calculation units, and the minimum virtual space calculation unit is denoted as a sub-cell unit. The subcell irradiance calculation module is used to calculate the total front irradiance and total back irradiance of each subcell, and to correct the incident angle of light on the subcell. The subcell cell current-voltage characteristic curve calculation module is used to electrically couple the front and back sides of the subcell cell to obtain the current-voltage characteristic curve of a single subcell cell. The current-voltage characteristic curve coupling module is used to couple the current-voltage characteristic curves of the sub-cell cells step by step to obtain the current-voltage characteristic curves of the target level. The power generation prediction module is used to solve for the maximum output power based on the current-voltage characteristic curve of the target level, and to calculate the predicted power generation within the target time interval based on the maximum output power at different times.

9. A photovoltaic module power generation prediction device under complex shading conditions, characterized in that, The photovoltaic module power generation prediction device under complex shading conditions includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor to implement the photovoltaic module power generation prediction method under complex shading conditions as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the photovoltaic module power generation prediction method under complex shading conditions as described in any one of claims 1-7.

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