Roof photovoltaic system power generation prediction method and system considering heat power snow melting process

Abstract

The present disclosure provides a kind of roof photovoltaic system power generation prediction method and system considering heat snow melting process, belong to photovoltaic system power generation technical field.The method comprises: the historical meteorological data of target area is collected, and typical meteorological combination condition is extracted by cluster analysis, and is set with the background meteorological condition of target area snow thickness composition;Build the multi-node coupling dynamic heat transfer model of snow stage and no snow stage, including roof, photovoltaic panel, energy conservation and snow freeze-thaw mass conservation of snow;According to snow temperature and cumulative positive energy threshold, the snow melting process is divided into dry snow stage, phase change stage and no snow stage, and the corresponding model is dynamically switched for dynamic simulation prediction;Obtain the photovoltaic panel power generation of different background meteorological conditions, and snow completely melting time.

Description

Technical Field

[0001] This disclosure pertains to the field of building energy conservation and new energy technology, and in particular relates to a method and system for predicting the power generation of a rooftop photovoltaic system that takes into account the thermal snow melting process. Background Technology

[0002] Rooftop photovoltaic (PV) systems are a distributed energy technology solution that uses brackets to elevate photovoltaic panels to the space above a roof. Because the load-bearing capacity of existing rural buildings is generally poor, directly erecting brackets on the roof not only increases the additional load on the roof but also often damages the original waterproofing layer due to the need for penetrating fixing during bracket installation, easily leading to leaks if not handled properly. Therefore, in engineering applications, ground-mounted brackets are typically used to lift the photovoltaic panels entirely above the roof.

[0003] In high-latitude rural areas with frequent snowfall, a large amount of snow that should have fallen on the roof ends up on the photovoltaic surface. The accumulated snow affects the thermoelectric performance of the system, that is, the heat transfer characteristics and photoelectric conversion capacity of the rooftop photovoltaic system under real meteorological conditions when snow is present. On the one hand, this includes the dynamic heat exchange process involving multiple nodes between the environment, snow, photovoltaic panels and the roof. Specifically, this is reflected in the heat absorption and release during the freeze-thaw phase change of the snow, the convective and radiative heat lost by the entire system to the surrounding environment, and the dynamically changing heat flux from the roof interior to the exterior due to the influence of photovoltaics and snow. On the other hand, it refers to the photoelectric conversion process of the photovoltaic panels, that is, the heat flux of solar radiation converted into electrical energy in the model.

[0004] Meanwhile, after absorbing radiative and convective heat transfer from the surrounding environment and conductive heat transfer from the photovoltaic panels, the snow undergoes a dynamic thermodynamic evolution process of heating, phase change, and eventual sliding under the coupled heat transfer effect between the surrounding environment and the photovoltaic panels. Existing research usually focuses on the evolution characteristics of snow accumulation on ground-based photovoltaic systems or rooftops, while research on the multi-node coupled heat transfer and snow melting process evolution between the environment, snow, photovoltaics, and roof in rooftop photovoltaic systems is insufficient.

[0005] Therefore, a method and system for predicting the power generation of rooftop photovoltaic systems that takes into account the thermal snow melting process is urgently needed. Summary of the Invention

[0006] To address the aforementioned technical problems, this disclosure provides a method and system for predicting the power generation of a rooftop photovoltaic system that takes into account the thermal snow melting process, for accurately predicting the power generation of the rooftop photovoltaic system during the snow melting process.

[0007] The purpose of this disclosure is achieved through the following technical solution: This disclosure presents a method for predicting the power generation of a rooftop photovoltaic system that takes into account the thermal snow melting process, including: After preprocessing the historical meteorological data of the target area, cluster analysis is performed to extract typical meteorological combination conditions. Then, the obtained typical meteorological conditions are combined with various snow thicknesses to form background meteorological conditions. Multi-node coupled heat transfer models were constructed for snowy and snowless stages respectively. The multi-node coupled heat transfer model for the snowy stage includes the energy conservation model of the roof, photovoltaic panels and snow, as well as the mass conservation model of snow during the freeze-thaw cycle. A multi-node coupled heat transfer model for the snowless phase, including an energy conservation model for the roof and an energy conservation model for the photovoltaic panels; Background meteorological conditions are input into a multi-node coupled heat transfer model for the snowy stage to calculate snow temperature and accumulated positive energy. Based on the set thresholds for snow temperature and accumulated positive energy, the snow melting process is divided into a dry snow stage, a snow phase change stage, and a snowless stage. The dry snow stage and the snow phase change stage are dynamically simulated and predicted using a multi-node coupled heat transfer model for the snowy stage. The snowless stage is dynamically simulated and predicted using a multi-node coupled heat transfer model for the snowless stage. Each stage is sequentially connected, and the corresponding model is switched in real time according to the snow condition until the snow completely melts. Based on the dynamic simulation prediction, the photovoltaic power generation under different background meteorological conditions and the time for complete snow melting are obtained.

[0008] Furthermore, the typical meteorological combination conditions are classified according to outdoor air temperature and solar radiation intensity, including low radiation extreme cold, low radiation cold, low radiation warm, moderate radiation extreme cold, moderate radiation cold, moderate radiation warm, high radiation extreme cold, high radiation cold, and high radiation warm; the historical meteorological data include total solar radiation and outdoor air temperature.

[0009] Furthermore, the energy conservation model for the roof is based on the outer surface of the roof as the thermal balance control surface. The change in roof heat storage is equal to the algebraic sum of the conductive heat flux absorbed by the outer surface of the roof from the indoor side and the long-wave radiation heat flux from the back of the photovoltaic panel to the roof, minus the value of the convective heat flux of the air in the interlayer between the outer surface of the roof and the back of the photovoltaic panel. The change in roof heat storage is the product of the roof mass, the roof specific heat capacity, and the rate of change of the roof outer surface temperature over time. The amount of conductive heat flux absorbed from the interior side by the outer surface of the roof is equal to the product of the total heat transfer coefficient of the roof, the roof per unit area, and the temperature difference between the inner and outer surfaces of the roof. The long-wave radiative heat flux from the back of the photovoltaic panel to the roof is equal to the Stefan-Boltzmann constant, the surface emissivity of the photovoltaic panel, the roof unit area, and the product of the fourth power of the photovoltaic panel temperature and the fourth power of the roof outer surface temperature; wherein, the surface emissivity of the photovoltaic panel is set to 0.9. The convective heat flux of the air sandwiched between the outer surface of the roof and the back of the photovoltaic panel is calculated using the following formula: , In the formula, This refers to the convective heat flux of the air sandwiched between the outer surface of the roof and the back of the photovoltaic panel. For the unit area of ​​the roof, The temperature of the outer surface of the roof. The temperature of the air in the interlayer between the photovoltaic panel and the roof; The damping ratio coefficient is the wind speed around the photovoltaic panel compared to the wind speed at the reference site, and its value is 0.2. For ambient wind speed.

[0010] Furthermore, the energy conservation model of the photovoltaic panel is divided into two states: no snow accumulation and snow accumulation. The energy conservation model for photovoltaic panels in the absence of snow accumulation is expressed as follows: The rate of change of heat storage of photovoltaic panels in the absence of snow accumulation is equal to the algebraic sum of the solar radiation heat flux absorbed by the front of the photovoltaic panel and the solar radiation heat flux reflected from the ground absorbed by the back of the photovoltaic panel, minus the long-wave radiation heat flux from the front of the photovoltaic panel to the sky, the long-wave radiation heat flux from the back of the photovoltaic panel to the roof, the convective heat transfer heat flux between the front of the photovoltaic panel and the surrounding air, the convective heat transfer heat flux between the back of the photovoltaic panel and the interlayer air, and the power generation of the photovoltaic panel. All heat flux and power generation are calculated based on the unit area of ​​the roof, and the sign of each item is positive when energy flows into the photovoltaic panel and negative when it flows out. The rate of change of heat storage of the snow-free photovoltaic panel is equal to the product of the photovoltaic panel's mass and its specific heat capacity, multiplied by the rate of change of its surface temperature over time.

[0011] Furthermore, the energy conservation model for photovoltaic panels with snow cover is expressed as follows: the rate of change of heat storage of photovoltaic panels with snow cover is equal to the sum of the solar radiation heat flux absorbed by the front of the photovoltaic panel and the solar radiation heat flux reflected from the ground absorbed by the back of the photovoltaic panel, minus the long-wave radiation heat flux from the back of the photovoltaic panel to the roof, the convective heat transfer heat flux between the back of the photovoltaic panel and the interlayer air, the contact conduction heat flux between the photovoltaic panel and the bottom snow, and the power generation of the photovoltaic panel. The solar radiation heat flux absorbed by the front of the photovoltaic panel is equal to the product of the solar radiation intensity received by the front of the photovoltaic panel, the light transmittance of the snow, the solar radiation absorptivity of the front of the photovoltaic panel, and the unit area of ​​the photovoltaic panel. The light transmittance of snow is a constant. The base is the product of the negative extinction coefficient of the snow and the real-time total thickness of the snow. The heat flux through contact conduction between the photovoltaic panel and the underlying snow is equal to the product of the interfacial heat transfer coefficient between the photovoltaic panel and the snow, the temperature difference between the photovoltaic panel and the snow, and the dynamic reduction factor. The dynamic reduction factor is set to 1.0 during the snow phase change stage and 0.3 during the dry snow stage. The interfacial heat transfer coefficient between the photovoltaic panel and the snow is equal to 2 multiplied by the dynamic thermal conductivity of the snow and then divided by the real-time total thickness of the snow. The power generation of photovoltaic panels is calculated using the following formula: , In the formula, The amount of electricity generated by the photovoltaic panels; The solar radiation heat flux absorbed by the front of the photovoltaic panel; The heat flux of solar radiation reflected from the ground absorbed by the back of the photovoltaic panel; The photoelectric conversion efficiency of the photovoltaic panel under standard test conditions; The power temperature coefficient; Temperature of the photovoltaic panel; All heat flux and power generation are calculated based on the unit area of ​​the roof, and the sign of each item is positive when energy flows into the photovoltaic panel and negative when it flows out. The rate of change of heat storage of the photovoltaic panel with snow accumulation is equal to the product of the mass of the photovoltaic panel and its specific heat capacity, multiplied by the rate of change of its surface temperature over time.

[0012] Furthermore, the real-time total thickness of the snow is determined based on the equivalent density of the snow in the snow freeze-thaw cycle. The equivalent density of the snow is equal to the sum of the real-time masses of dry snow, ice and liquid water in the snow layer, divided by the product of the real-time total thickness of the snow and the unit area of ​​the roof. The dynamic thermal conductivity of snow is calculated using the following formula: , , In the formula, The dynamic thermal conductivity of the snow is given. The dynamic equivalent weight of snow cover; This represents the real-time equivalent density during the snow freeze-thaw cycle.

[0013] Furthermore, the energy conservation model of the snow is expressed as follows: the net heat flux absorbed by the snow is equal to the product of the snow mass and its specific heat capacity multiplied by the rate of change of its temperature over time; the rate of change of heat storage is equal to the algebraic sum of the solar radiation heat flux absorbed by the upper surface of the snow and the contact conduction heat flux between the photovoltaic panel and the bottom snow, minus the difference between the atmospheric convection heat flux between the upper surface of the snow and the surrounding air and the long-wave radiation heat flux from the upper surface of the snow to the sky.

[0014] Furthermore, the mass conservation model for the snow accumulation during the freeze-thaw cycle is as follows: The change in liquid water mass is equal to the mass of snow melted during the current calculation time step minus the mass of ice formed and the mass of lost meltwater runoff. The sum of the changes in the mass of dry snow and ice equals the mass of ice formed in the current calculation time step minus the mass of melted snow. The sum of the changes in the mass of liquid water, dry snow, and ice is equal to the negative of the mass of meltwater runoff lost in the current calculation time step; The mass of snow melted within the current calculation time step is equal to the net heat flux absorbed by the snow within that time step multiplied by the time step length, and then divided by the latent heat of phase change of the snow. The ice mass within the current calculation time step is equal to the absolute value of the net heat flux of snow accumulation within that time step multiplied by the time step length, and then divided by the latent heat of phase change of snow melting. The mass of meltwater runoff lost within the current calculation time step is the meltwater seepage velocity within the snow cover multiplied by the time step. The specific formula for calculating the seepage velocity of meltwater within snow cover is as follows: , In the formula, The density of water; The dynamic permeability of snow cover; It is the acceleration due to gravity; The installation tilt angle of the photovoltaic panels; The dynamic viscosity of liquid water; This represents the current water saturation level of the snow cover. The irreducible water saturation level is set to 5%. The dynamic permeability of the snow is calculated using the following formula: , In the formula, For reference permeability, take m²; The real-time porosity of the snow cover; The density of dry snow; This is the density of ice.

[0015] Furthermore, the cumulative positive energy absorbed by the snow is calculated using the following formula: , In the formula, The accumulated positive energy absorbed by the snow. The cumulative positive energy threshold; when It was determined that the snow had slid off the surface of the photovoltaic panel.

[0016] This disclosure discloses a prediction system for the thermal snow melting process and thermoelectric performance of a rooftop photovoltaic system, used to execute the method, including: The data acquisition module is used to collect historical meteorological data for the target area, including total solar radiation and outdoor air temperature; The processing module interacts with the acquisition module to preprocess the historical meteorological data, perform cluster analysis on the preprocessed historical meteorological data, extract typical meteorological combination conditions, and combine them with various snow thicknesses to form background meteorological conditions. The model building module interacts with the processing module to construct multi-node coupled heat transfer models for snowy and snowless stages. The multi-node coupled heat transfer model for the snowy stage includes an energy conservation model for the roof, photovoltaic panels, and snow accumulation, as well as a mass conservation model for the snow accumulation during the freeze-thaw cycle. The multi-node coupled heat transfer model for the snowless stage includes an energy conservation model for the roof and an energy conservation model for the photovoltaic panels. The dynamic simulation and prediction module interacts with the model building module to input the background meteorological conditions as boundary conditions into the multi-node coupled heat transfer model of the snowy stage. It calculates the transient temperature and cumulative positive energy of the snow accumulation and divides the snowmelt process into a dry snow stage, a snow phase transition stage, and a snowless stage based on preset temperature and cumulative positive energy thresholds. The dry snow stage and the snow phase transition stage use the multi-node coupled heat transfer model of the snowy stage for dynamic simulation and prediction, while the snowless stage switches to the multi-node coupled heat transfer model of the snowless stage for dynamic simulation and prediction. Each stage is sequentially connected, and the corresponding model is switched based on the real-time determination of the snow accumulation status until the snow completely melts. Based on the above dynamic simulation and prediction results, the photovoltaic power generation and the time for complete snow melting under different background meteorological conditions were obtained.

[0017] The beneficial effects of this disclosure are as follows: 1. This disclosure considers the snow melting process on rooftop photovoltaic systems and establishes a multi-node coupled dynamic heat transfer model for the snow-covered and snowless stages of the interconnected heat transfer between the surrounding environment, snow, photovoltaics, and the roof. Using this model, the power generation of photovoltaic systems under different background meteorological conditions can be accurately predicted. This effectively avoids problems such as overestimating actual photovoltaic power generation and misunderstanding the system's impact on the surrounding environment caused by not considering snow phase change in winter, thus improving the assessment bias of rooftop heat loss.

[0018] 2. This disclosure, while predicting the power generation of photovoltaic panels under different background meteorological conditions, can also evolve the snow freeze-thaw phase change process in the rooftop photovoltaic system to obtain the time for complete snow melting, providing an effective tool for accurately assessing the impact of photovoltaic power generation on building energy conservation. Attached Figure Description

[0019] Figure 1 This disclosure includes a flowchart illustrating the method.

[0020] Figure 2 This is a diagram showing the actual snow thickness, moisture content, and state analysis during the snow freeze-thaw process in Example 1.

[0021] Figure 3 This is a schematic diagram of the positive energy curve of snow accumulation during the snow freeze-thaw process in Example 1.

[0022] Figure 4 The photovoltaic power generation assessment diagram in Example 1.

[0023] Figure 5 This is a temperature change curve from Example 1.

[0024] Figure 6 This is a system block diagram of the prediction system described in this disclosure. Detailed Implementation

[0025] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the disclosure will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other. The disclosure will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of this disclosure and should not be used to limit the scope of protection of this disclosure.

[0026] Example 1: As Figures 1-5 As shown, this disclosure presents a method for predicting the power generation of a rooftop photovoltaic system that takes into account the thermal snow melting process, including: Data Acquisition: Collect historical meteorological data for the target area, including total solar radiation and outdoor air temperature; Data processing: After preprocessing the historical meteorological data, cluster analysis is performed to extract typical meteorological combination conditions. The obtained typical meteorological conditions are then combined with various snow thicknesses to form multiple sets of different background meteorological conditions. Multi-node coupled heat transfer models are constructed for snowy and snowless stages respectively. The multi-node coupled heat transfer model for the snowy stage includes an energy conservation model for the roof, photovoltaic panels and snow, as well as a mass conservation model for snow during the freeze-thaw cycle. A multi-node coupled heat transfer model for the snowless phase, including an energy conservation model for the roof and an energy conservation model for the photovoltaic panels; Dynamic simulation and prediction: The background meteorological conditions are used as boundary conditions and input into a multi-node coupled dynamic heat transfer model for the snowy stage. The transient temperature and cumulative positive energy of the snow are calculated. Based on preset temperature and cumulative positive energy thresholds, the snow melting process is divided into a dry snow stage, a snow phase change stage, and a snowless stage. The dry snow stage and the snow phase change stage are dynamically simulated and predicted using a multi-node coupled heat transfer model for the snowy stage. The snowless stage is dynamically simulated and predicted using a multi-node coupled heat transfer model for the snowless stage. Each stage is sequentially connected, and the corresponding model is switched in real time according to the snow condition until the snow is completely melted. Based on the dynamic simulation prediction, the photovoltaic power generation under different background meteorological conditions and the time for complete snow melting are obtained; that is, the cumulative predicted value of photovoltaic power generation and the predicted value of the time for complete snow melting are output.

[0027] The typical meteorological combination conditions are obtained by preprocessing historical meteorological data of the target area, performing cluster analysis, and using outdoor air temperature and solar radiation intensity as the classification criteria. These typical meteorological combination conditions include low-radiation extreme cold, low-radiation cold, low-radiation warm, moderate-radiation extreme cold, moderate-radiation cold, moderate-radiation warm, high-radiation extreme cold, high-radiation cold, and high-radiation warm. The historical meteorological data includes total solar radiation and outdoor air temperature. Snow thickness is set to 0.5cm, 1cm, 1.5cm, 2cm, 3.5cm, 5cm, 7cm, 10.5cm, 13cm, 15cm, 25cm, and 35cm. Finally, the obtained typical meteorological combinations are arranged and combined with each snow thickness to obtain the background meteorological conditions.

[0028] The preprocessing (existing technology) includes outlier removal and missing value imputation: missing values ​​are checked on the original time series data, and outliers are identified and removed based on the temporal variation patterns of meteorological elements; for missing data, linear interpolation is used to imput them to ensure the temporal continuity and integrity of the historical meteorological dataset.

[0029] Given the significant differences in dimensions and numerical ranges between solar radiation and outdoor air temperature, in order to avoid a single feature dominating the clustering, the clustering analysis employs the Z-score method to standardize the preprocessed historical meteorological data, thereby eliminating the influence of dimensions and obtaining standardized historical meteorological data.

[0030] Euclidean distance was used as the similarity metric, and the K-Means algorithm was applied for aggregation analysis. The elbow rule was used to observe the inflection point of the sum of squared errors within clusters, and the silhouette coefficient was used to evaluate the intra-cluster compactness and inter-cluster separation. Finally, the optimal number of clusters K was determined, and typical meteorological combination conditions based on outdoor air temperature and solar radiation intensity were summarized.

[0031] In this example, a multi-node coupled heat transfer model is constructed for both the snowy and snowless stages. The lumped parameter method is used for modeling to ensure that the core characteristics of the snow accumulation phase change are captured and that computational accuracy is maintained while reducing solution complexity. The computation time step is set to 10 seconds. The models are as follows: The energy conservation model is determined based on the conservation of total energy input and output from the roof, photovoltaic panels, and snow accumulation. The energy conservation model for the roof uses the outer surface of the roof as the thermal balance control surface. The change in roof heat storage is equal to the algebraic sum of the conductive heat flux absorbed by the outer surface of the roof from the indoor side and the long-wave radiation heat flux from the back of the photovoltaic panels to the roof, minus the convective heat flux of the air in the interlayer between the outer surface of the roof and the back of the photovoltaic panels. The change in roof heat storage is the product of the roof mass and the roof's specific heat capacity multiplied by the rate of change of the roof's outer surface temperature over time. The specific formula is as follows: , In the formula, The conductive heat flux absorbed from the indoor side by the outer surface of the roof; This refers to the long-wave radiation heat flux from the back of the photovoltaic panel to the roof. The convective heat flux is the airflow between the outer surface of the roof and the back of the photovoltaic panel. For the quality of the roof; The specific heat capacity of the roof; The external surface temperature of the roof. The amount of conductive heat flux absorbed from the interior side by the external surface of the roof is equal to the product of the total heat transfer coefficient of the roof, the roof area per unit area, and the temperature difference between the internal and external surface temperatures of the roof; wherein, the internal surface temperature of the roof is set to a constant value of 20℃; the specific formula is as follows: , In the formula, The total heat transfer coefficient of the roof; This is the temperature of the inner surface of the roof, which is controlled by the indoor distributed heating system and set according to the indoor temperature of the target area. In this example, it is set to 20℃. This refers to the unit area of ​​the roof.

[0032] The long-wave radiation heat flux from the back of the photovoltaic panel to the roof is determined according to the Stefan-Boltzmann radiation law. This long-wave radiation heat flux is equal to the Stefan-Boltzmann constant, the emissivity of the photovoltaic panel surface, the roof unit area, and the product of the fourth power of the photovoltaic panel temperature and the fourth power of the roof outer surface temperature; wherein, the emissivity of the photovoltaic panel surface is set to a fixed value of 0.9; the specific formula is as follows: , In the formula, It is the Stefan-Boltzmann constant; The surface emissivity of the photovoltaic panel is set to 0.9; This refers to the temperature of the photovoltaic panel.

[0033] The direction in which the photovoltaic panel points towards the outer surface of the roof is defined as the positive direction of the net long-wave radiation heat flux. When the surface temperature of the photovoltaic panel is higher than the temperature of the outer surface of the roof, the long-wave radiation heat flux takes a positive value, indicating that the net radiative energy flow is released from the photovoltaic panel and transferred to the roof; conversely, when the temperature of the photovoltaic panel is lower than the temperature of the roof, the heat flux takes a negative value, indicating that the net radiative energy flow is transferred from the roof to the photovoltaic panel.

[0034] The convective heat flux of the air sandwiched between the outer surface of the roof and the back of the photovoltaic panel is calculated using the following formula: , In the formula, The temperature of the air in the interlayer between the photovoltaic panel and the roof; The damping ratio coefficient is the wind speed around the photovoltaic panel compared to the wind speed at the reference site, and its value is 0.2. For ambient wind speed.

[0035] This disclosure calculates the roof's outer surface temperature at the current time step by solving the roof's energy conservation model.

[0036] The energy conservation model for photovoltaic panels is divided into two types: one with no snow accumulation and the other with snow accumulation. The energy conservation model for photovoltaic (PV) panels in a snow-free state is expressed as follows: The rate of change of heat storage of a snow-free PV panel equals the algebraic sum of the solar radiation heat flux absorbed by the front of the PV panel and the solar radiation heat flux reflected from the ground absorbed by the back of the PV panel, minus the long-wave radiation heat flux from the front of the PV panel to the sky, the long-wave radiation heat flux from the back of the PV panel to the roof, the convective heat transfer flux between the front of the PV panel and the surrounding air, the convective heat transfer flux between the back of the PV panel and the interlayer air, and the power generation of the PV panel. The specific formula is as follows: , In the formula, For the quality of photovoltaic panels; This refers to the specific heat capacity of the photovoltaic panel. Temperature of the photovoltaic panel; The solar radiation heat flux absorbed by the front of the photovoltaic panel; The heat flux of solar radiation reflected from the ground absorbed by the back of the photovoltaic panel; The long-wave radiation heat flux of the photovoltaic panel facing the sky; This refers to the long-wave radiation heat flux from the back of the photovoltaic panel to the roof. This refers to the convective heat transfer flux between the front of the photovoltaic panel and the surrounding air. This refers to the convective heat transfer flux between the back of the photovoltaic panel and the interlayer air. The amount of electricity generated by the photovoltaic panels; The solar radiation heat flux absorbed by the front of the photovoltaic panel is equal to the product of the solar radiation intensity received by the front of the photovoltaic panel, the solar radiation absorptivity of the photovoltaic panel surface, and the unit area of ​​the photovoltaic panel; the solar radiation heat flux absorbed by the back of the photovoltaic panel is equal to the product of the solar radiation heat flux absorbed by the front of the photovoltaic panel, a preset proportionality coefficient, and the unit area of ​​the photovoltaic panel, wherein the preset proportionality coefficient is the ratio of ground-reflected solar radiation absorbed by the back of the photovoltaic panel to the solar radiation absorbed by the front of the photovoltaic panel; the specific formula is as follows: , , In the formula, The intensity of solar radiation received on the front of the photovoltaic panel; The solar radiation absorption rate on the front of the photovoltaic panel is set to 0.8. This refers to the unit area of ​​the photovoltaic panel; The proportion of solar radiation reflected from the ground absorbed by the back of the photovoltaic panel to the solar radiation absorbed by the front of the photovoltaic panel is set to 10%. The formula for the long-wave radiation heat flux of a photovoltaic panel facing the sky is: , In the formula, It is the Stefan-Boltzmann constant; The emissivity of the photovoltaic panel surface is taken as 0.9; Temperature of the photovoltaic panel; Outdoor air temperature; This is the cloud cover correction factor; The long-wave radiative heat flux from the back of the photovoltaic panel to the roof is calculated using the following formula: , In the formula, It is the Stefan-Boltzmann constant; The surface emissivity of the photovoltaic panel is set to 0.9; For the unit area of ​​the roof; Temperature of the photovoltaic panel; This refers to the temperature of the outer surface of the roof.

[0037] The convective heat transfer flux between the front of the photovoltaic panel and the surrounding air, and between the back of the photovoltaic panel and the air in the interlayer between the roof and the photovoltaic panel, is expressed by the following formula: , , In the formula, The temperature of the air in the interlayer between the photovoltaic panel and the roof; The formula for the power generation of photovoltaic panels in the absence of snow is as follows: , In the formula, The photoelectric conversion efficiency of the photovoltaic panel under standard test conditions is set to 0.18. Let be the power temperature coefficient of the photovoltaic panel, set to -0.45% / ℃.

[0038] When there is snow accumulation, the snow acts as a physical barrier, affecting the solar radiation received by the photovoltaic panel. The energy conservation equation for the photovoltaic panel is also influenced by the latent heat of phase change from the snow and contact heat conduction. The energy conservation model for a photovoltaic panel with snow accumulation is expressed as follows: The rate of change of heat storage of a photovoltaic panel with snow accumulation equals the sum of the solar radiation heat flux absorbed by the front of the photovoltaic panel and the solar radiation heat flux reflected from the ground absorbed by the back of the photovoltaic panel, minus the long-wave radiation heat flux from the back of the photovoltaic panel to the roof, the convective heat transfer heat flux between the back of the photovoltaic panel and the interlayer air, the contact conduction heat flux between the photovoltaic panel and the underlying snow, and the difference in power generation from the photovoltaic panel. The calculation formula is as follows: , In the formula, The solar radiation heat flux absorbed by the front of the photovoltaic panel; This refers to the heat flux conducted through contact between the photovoltaic panel and the underlying snow. The solar radiation heat flux absorbed by the front of a photovoltaic panel is equal to the product of the solar radiation intensity received by the front of the photovoltaic panel, the light transmittance of the snow, the solar radiation absorptivity of the front of the photovoltaic panel, and the unit area of ​​the photovoltaic panel; the formula is as follows: , In the formula, The light transmittance of snow is a constant. Using the negative extinction coefficient of snow as the base and the product of the real-time total snow thickness as the exponent, the calculation formula is as follows: , In the formula, The light transmittance of snow; The extinction coefficient of snow accumulation is set to 40m. -1 ; This represents the real-time total thickness of the snow cover. The heat flux of solar radiation reflected from the ground absorbed by the back of the photovoltaic panel, the long-wave radiation heat flux from the back of the photovoltaic panel to the roof, and the convective heat transfer heat flux between the back of the photovoltaic panel and the interlayer air are all calculated using the same formulas as in the energy conservation model of the photovoltaic panel in the absence of snow accumulation.

[0039] The heat flux through contact conduction between the photovoltaic panel and the underlying snow is equal to the product of the interfacial heat transfer coefficient between the photovoltaic panel and the snow, the temperature difference between the photovoltaic panel and the snow, and the dynamic reduction factor; the specific calculation formula is as follows: , In the formula, This refers to the heat flux conducted through contact between the photovoltaic panel and the underlying snow. The interfacial heat transfer coefficient between the photovoltaic panel and the snow accumulation; Temperature of snow cover; The dynamic reduction factor is set to 1.0 during the snow phase change stage, taking into account the effect of meltwater seeping into the capillary action to form a water film; and to 0.3 during the dry snow stage. The interfacial heat transfer coefficient between the photovoltaic panel and the snow is equal to 2 multiplied by the dynamic thermal conductivity of the snow, and then divided by the real-time total thickness of the snow; the specific calculation formula is as follows: , In the formula, The interfacial heat transfer coefficient between the photovoltaic panel and the snow accumulation; The dynamic thermal conductivity of the snow cover; The real-time total snow thickness is determined based on the equivalent density of the snow. The equivalent density of the snow is equal to the sum of the real-time masses of dry snow, ice, and liquid water in the snow layer, divided by the product of the real-time total snow thickness and the unit area of ​​the roof. The calculation formula is as follows: , In the formula, The real-time equivalent density during the snow freeze-thaw cycle; , , The real-time masses of dry snow, ice, and liquid water are respectively. The dynamic thermal conductivity of snow is calculated based on the equivalent density of the snow, and the specific calculation formula is as follows: , , In the formula, The dynamic equivalent weight of snow cover; The real-time equivalent density during the snow freeze-thaw cycle; The power generation of photovoltaic panels under snow cover conditions is calculated using the following formula: , In the formula, The amount of electricity generated by the photovoltaic panels; The solar radiation heat flux absorbed by the front of the photovoltaic panel; The heat flux of solar radiation reflected from the ground absorbed by the back of the photovoltaic panel; The photoelectric conversion efficiency of the photovoltaic panel under standard test conditions; The power temperature coefficient; Temperature of the photovoltaic panel; All heat flux and electrical power heat flux are calculated based on the unit area of ​​the roof, and the sign of each item is positive when energy flows into the photovoltaic panel and negative when it flows out. The rate of change of heat storage of the photovoltaic panel with and without snow accumulation is equal to the product of the photovoltaic panel's mass and its specific heat capacity, multiplied by the rate of change of its surface temperature over time.

[0040] This disclosure calculates the photovoltaic panel temperature and power generation by solving the energy conservation model of the photovoltaic panel under conditions of no snow or snow accumulation, and then evaluates the photovoltaic power generation capacity and loss rate.

[0041] The energy conservation model for snow cover is expressed as follows: the net heat flux absorbed by the snow cover changes its internal energy, and its value is equal to the rate of change of the snow's heat storage. This rate of change is the product of the snow's mass and its specific heat capacity multiplied by the rate of change of its temperature over time. The rate of change of heat storage is equal to the algebraic sum of the solar radiation heat flux absorbed by the snow's upper surface and the contact conduction heat flux between the photovoltaic panel and the underlying snow, minus the atmospheric convection heat flux between the snow's upper surface and the surrounding air, and the long-wave radiation heat flux from the snow's upper surface to the sky. The specific formula is as follows: , In the formula, Net heat flux absorbed by snow cover; The mass of the snow cover; The specific heat capacity of snow; Temperature of snow cover; The solar radiation heat flux absorbed by the upper surface of the snow; This refers to the heat flux conducted through contact between the photovoltaic panel and the underlying snow. This refers to the atmospheric convective heat transfer flux between the upper surface of the snow and the surrounding air. This represents the long-wave radiation heat transfer flux from the upper surface of the snow to the sky.

[0042] The specific formula for the solar radiation heat flux absorbed by the upper surface of snow is as follows: , In the formula, The solar radiation absorptivity of the snow surface is set to 0.3; The light transmittance of snow; The specific formula for calculating the heat flux through contact conduction between the photovoltaic panel and the underlying snow is as follows: , In the formula, This refers to the heat flux conducted through contact between the photovoltaic panel and the underlying snow. The interfacial heat transfer coefficient between the photovoltaic panel and the snow accumulation; Temperature of the photovoltaic panel; Temperature of snow cover; The reduction factor r is a dynamic reduction factor. In the water-bearing stage, considering the effect of meltwater seeping into the capillary and forming a water film, the reduction factor r is set to 1.0; in the dry snow stage, it is set to 0.3. The atmospheric convective heat transfer flux between the upper surface of the snow cover and the surrounding air is calculated using the following formula: , In the formula, Outdoor air temperature; The long-wave radiation heat transfer flux from the upper surface of snow to the sky is given by the following formula: , In the formula, It is the Stefan-Boltzmann constant; Emissivity of snow cover; This is the cloud cover correction factor.

[0043] The mass conservation model for snow accumulation during the freeze-thaw cycle is based on the process of snow melting into water, freezing into ice, and then sliding off the photovoltaic panel due to insufficient friction caused by continuous melting during heat absorption and release. The model calculates the real-time masses of dry snow, ice, and liquid water to determine the mass conservation of snow accumulation. Specifically, it includes the following mass conservation equations for liquid water, ice, and dry snow: the change in liquid water mass equals the mass of melted snow minus the mass of ice and lost meltwater runoff in the current calculation time step; the sum of the changes in dry snow and ice mass equals the mass of ice minus the mass of melted snow in the current calculation time step; and the sum of the changes in liquid water, dry snow, and ice mass equals the negative of the mass of lost meltwater runoff in the current calculation time step. This achieves mass conservation for the entire physical process. The specific formulas are as follows: , , , In the formula, This represents the change in the mass of liquid water. This represents the mass of snow melted within the current calculation time step. This represents the ice mass within the current calculation time step. For the real-time quality of dry snow; For the real-time quality of ice; This represents the mass of meltwater runoff lost within the current calculation time step. This represents the change in the mass of dry snow. This represents the change in the mass of ice. When the snow temperature reaches the melting threshold of 0℃, it enters the freeze-thaw phase transition stage, where the absorbed or released heat is converted into latent heat of phase change, and the snow will melt. The mass of snow melted within the current calculation time step is equal to the net heat flux absorbed by the snow within that time step multiplied by the time step length, and then divided by the latent heat of phase change of the snow. The calculation formula is as follows: , In the formula, To calculate the time step, set it to 10 seconds; The latent heat of phase transition for snow melting is set at 334 kJ / kg.

[0044] The mass of ice formation within the current calculation time step is equal to the absolute value of the net heat flux of snow accumulation within that time step multiplied by the time step length, and then divided by the latent heat of snow melting phase change; the calculation formula is as follows: , In the formula, Net heat flux absorbed by snow cover; Meltwater within the snow layer is retained through capillary action. Once the irreducible water saturation level is exceeded, seepage occurs. Based on Darcy's law, the mass of meltwater runoff lost in the current calculation time step is the meltwater seepage velocity within the snow layer multiplied by the time step, calculated as follows: , The formula for calculating the seepage velocity of meltwater within snow cover is as follows: , In the formula, The density of water; The dynamic permeability of snow cover; It is the acceleration due to gravity; The installation tilt angle of the photovoltaic panel (41° in this example); The dynamic viscosity of liquid water; This represents the current water saturation level of the snow cover. The irreducible water saturation level is set to 5%. The dynamic permeability of the snow is calculated using the following formula: , In the formula, For reference permeability, take m²; The real-time porosity of the snow cover; The density of dry snow; This is the density of ice.

[0045] This disclosure solves the energy conservation model of snow cover and the mass conservation model of snow cover in the freeze-thaw cycle process, dynamically calculates and updates the real-time equivalent density, dynamic thermal conductivity of snow cover, snow temperature, real-time mass of liquid water and ice, and mass of meltwater runoff in the freeze-thaw cycle of snow cover, and accumulates the cumulative positive energy (APE) absorbed by snow cover to determine the overall snowfall time.

[0046] The cumulative positive energy absorbed by the snow is calculated using the following formula: , In the formula, The accumulated positive energy absorbed by the snow; The set cumulative positive energy threshold, for Net heat flux absorbed by snow accumulation at all times; when When the system determines that the snow has completely slid off the photovoltaic panel surface, the prediction system will reset the snow thickness and mass to zero and switch back to the photovoltaic panel heat transfer model with no snow accumulation. For the 41° tilt photovoltaic panel in this disclosure... It is set to 1.1 MJ / m².

[0047] As snow continuously freezes and thaws, the amount of liquid water increases, gradually reducing the friction between the snow and the photovoltaic panel surface. Eventually, the attached material slides off the photovoltaic panel. The criterion for determining whether the snow has slid off completely is: when the accumulated positive energy absorbed by the snow reaches a trigger threshold, the attached material is considered to have slid off completely. A multi-node coupled heat transfer model for the appropriate stage is selected for dynamic simulation and prediction.

[0048] Figure 2 This is a graph showing the actual snow thickness, water content, and state analysis during the snow freeze-thaw process described in Example 1. The graph reflects the state change process of snow under set background meteorological conditions. It can be seen that under these conditions, the snow transitions from a dry snow state to a brief phase transition period, eventually sliding off, and the system subsequently stabilizes in a snow-free, bare state.

[0049] Figure 3 This is a schematic diagram of the positive energy accumulation curve of snow during the freeze-thaw process in Example 1. The photovoltaic panel surface has extremely high heat absorption efficiency, resulting in rapid accumulation of heat energy inside the snow. This figure reflects the change in the positive energy absorbed by the snow during the melting process over time. When the absorbed positive energy reaches the critical condition that triggers the snow to slide off, the snow falls off; after falling off, the accumulation of positive energy absorbed by the snow stops.

[0050] Figure 4 The photovoltaic panel power generation assessment graph in Example 1 shows the change in photovoltaic panel power generation over time during snow melting. When the snow is not completely melted, the light transmittance of the photovoltaic panel decreases, resulting in a reduction in power generation; as the snow falls off, the power generation returns to normal.

[0051] Figure 5 This is a temperature change curve from Example 1. The graph reflects the changes in the temperature of the photovoltaic panel and the outer surface temperature of the roof over time. It can be seen that during the day, after the photovoltaic panel is exposed to solar radiation, it heats up rapidly due to heat absorption, and its maximum temperature may exceed the ambient temperature; while the roof, due to its greater thermal inertia, exhibits lower lag and fluctuation in temperature change compared to the photovoltaic panel.

[0052] Based on the background meteorological conditions input in this example, dynamic simulation and prediction are performed to obtain the photovoltaic power generation under different background meteorological conditions, as well as the time for complete snow melting. That is, the cumulative predicted value of photovoltaic power generation and the predicted value of the time for complete snow melting are output.

[0053] Example 2: As Figure 6 As shown, a rooftop photovoltaic system power generation prediction system that takes into account the thermal snow melting process is used to execute the method, including: a data acquisition module 100, a processing module 200, a model building module 300, and a dynamic simulation prediction module 400. The data acquisition module 100 is used to collect historical meteorological data of the target area, including total solar radiation and outdoor air temperature; The processing module 200 interacts with the acquisition module to preprocess the historical meteorological data, perform cluster analysis on the preprocessed historical meteorological data, extract typical meteorological combination conditions, and combine them with various snow thicknesses to form background meteorological conditions. The model building module 300 interacts with the processing module to build multi-node coupled heat transfer models for snowy and snowless stages. The multi-node coupled heat transfer model for snowy stages includes an energy conservation model for the roof, photovoltaic panels, and snow accumulation, as well as a mass conservation model for snow accumulation during freeze-thaw cycles. The multi-node coupled heat transfer model for snowless stages includes an energy conservation model for the roof and an energy conservation model for the photovoltaic panels. The dynamic simulation prediction module 400 interacts with the model building module to input the background meteorological conditions as boundary conditions into the multi-node coupled heat transfer model of the snowy stage, calculate the transient temperature and cumulative positive energy of the snow, and divide the snow melting process into the dry snow stage, the snow phase change stage and the snowless stage according to the preset temperature threshold and cumulative positive energy threshold. The dry snow stage and the snow phase change stage call the multi-node coupled heat transfer model of the snow-covered stage for dynamic simulation and prediction. The snowless stage switches to the multi-node coupled heat transfer model of the snowless stage for dynamic simulation and prediction. Each stage is connected in sequence, and the corresponding model is switched according to the real-time judgment result of the snow status until the snow is completely melted. Based on the above dynamic simulation and prediction results, the photovoltaic power generation and the time for complete snow melting under different background meteorological conditions are obtained; and the predicted values ​​of snow melting completion time, roof heat loss coefficient and cumulative photovoltaic power generation are output accordingly, so as to complete the comprehensive quantitative evaluation of the thermal performance and power generation performance of the roof photovoltaic system under snow melting conditions.

[0054] The parts not described in detail in this application are all existing conventional technologies and will not be elaborated here.

[0055] It is understood that the above specific description of this disclosure is only for illustrating this disclosure and is not limited to the technical solutions described in the embodiments of this disclosure. Those skilled in the art should understand that modifications or equivalent substitutions can still be made to this disclosure to achieve the same technical effect; as long as the usage needs are met, they are all within the protection scope of this disclosure.

Claims

1. A method for predicting the power generation of a rooftop photovoltaic system that takes into account the thermal snow melting process, characterized in that: include: After preprocessing the historical meteorological data of the target area, cluster analysis is performed to extract typical meteorological combination conditions. Then, the obtained typical meteorological conditions are combined with various snow thicknesses to form background meteorological conditions. Multi-node coupled heat transfer models were constructed for snowy and snowless stages respectively. The multi-node coupled heat transfer model for the snowy stage includes the energy conservation model of the roof, photovoltaic panels and snow, as well as the mass conservation model of snow during the freeze-thaw cycle. A multi-node coupled heat transfer model for the snowless phase, including an energy conservation model for the roof and an energy conservation model for the photovoltaic panels; Background meteorological conditions are input into a multi-node coupled heat transfer model for the snowy stage to calculate snow temperature and accumulated positive energy. Based on the set thresholds for snow temperature and accumulated positive energy, the snow melting process is divided into a dry snow stage, a snow phase change stage, and a snowless stage. The dry snow stage and the snow phase change stage are dynamically simulated and predicted using a multi-node coupled heat transfer model for the snowy stage. The snowless stage is dynamically simulated and predicted using a multi-node coupled heat transfer model for the snowless stage. Each stage is sequentially connected, and the corresponding model is switched in real time according to the snow condition until the snow completely melts. Based on the dynamic simulation prediction, the photovoltaic power generation under different background meteorological conditions and the time for complete snow melting are obtained.

2. The method according to claim 1, characterized in that: The typical meteorological combination conditions are classified according to outdoor air temperature and solar radiation intensity, including low radiation extreme cold, low radiation cold, low radiation warm, moderate radiation extreme cold, moderate radiation cold, moderate radiation warm, high radiation extreme cold, high radiation cold, and high radiation warm; the historical meteorological data include total solar radiation and outdoor air temperature.

3. The method according to claim 1, characterized in that: The energy conservation model for the roof uses the outer surface of the roof as the thermal balance control surface. The change in roof heat storage is equal to the algebraic sum of the conductive heat flux absorbed by the outer surface of the roof from the indoor side and the long-wave radiation heat flux from the back of the photovoltaic panel to the roof, minus the value of the convective heat flux of the air in the interlayer between the outer surface of the roof and the back of the photovoltaic panel. The change in roof heat storage is the product of the roof mass, the roof specific heat capacity, and the rate of change of the roof outer surface temperature over time. The amount of conductive heat flux absorbed from the interior side by the outer surface of the roof is equal to the product of the total heat transfer coefficient of the roof, the roof per unit area, and the temperature difference between the inner and outer surfaces of the roof. The long-wave radiative heat flux from the back of the photovoltaic panel to the roof is equal to the Stefan-Boltzmann constant, the surface emissivity of the photovoltaic panel, the roof unit area, and the product of the fourth power of the photovoltaic panel temperature and the fourth power of the roof outer surface temperature; wherein, the surface emissivity of the photovoltaic panel is set to 0.

9. The convective heat flux of the air sandwiched between the outer surface of the roof and the back of the photovoltaic panel is calculated using the following formula: ， In the formula, This refers to the convective heat flux of the air sandwiched between the outer surface of the roof and the back of the photovoltaic panel. For the unit area of ​​the roof, The temperature of the outer surface of the roof. The temperature of the air in the interlayer between the photovoltaic panel and the roof; The damping ratio coefficient is the wind speed around the photovoltaic panel compared to the wind speed at the reference site, and its value is 0.

2. For ambient wind speed.

4. The method according to claim 1, characterized in that: The energy conservation model of the photovoltaic panel is divided into two types: no snow accumulation state and snow accumulation state. The energy conservation model for photovoltaic panels in the absence of snow accumulation is expressed as follows: The rate of change of heat storage of photovoltaic panels in the absence of snow accumulation is equal to the algebraic sum of the solar radiation heat flux absorbed by the front of the photovoltaic panel and the solar radiation heat flux reflected from the ground absorbed by the back of the photovoltaic panel, minus the long-wave radiation heat flux from the front of the photovoltaic panel to the sky, the long-wave radiation heat flux from the back of the photovoltaic panel to the roof, the convective heat transfer heat flux between the front of the photovoltaic panel and the surrounding air, the convective heat transfer heat flux between the back of the photovoltaic panel and the interlayer air, and the power generation of the photovoltaic panel. All heat flux and power generation are calculated based on the unit area of ​​the roof, and the sign of each item is positive when energy flows into the photovoltaic panel and negative when it flows out. The rate of change of heat storage of the snow-free photovoltaic panel is equal to the product of the photovoltaic panel's mass and its specific heat capacity, multiplied by the rate of change of its surface temperature over time.

5. The method according to claim 4, characterized in that: The energy conservation model for photovoltaic panels with snow cover is expressed as follows: The rate of change of heat storage of photovoltaic panels with snow cover is equal to the sum of the solar radiation heat flux absorbed by the front of the photovoltaic panel and the solar radiation heat flux reflected from the ground absorbed by the back of the photovoltaic panel, minus the long-wave radiation heat flux from the back of the photovoltaic panel to the roof, the convective heat transfer heat flux between the back of the photovoltaic panel and the interlayer air, the contact conduction heat flux between the photovoltaic panel and the bottom snow, and the power generation of the photovoltaic panel. The solar radiation heat flux absorbed by the front of the photovoltaic panel is equal to the product of the solar radiation intensity received by the front of the photovoltaic panel, the light transmittance of the snow, the solar radiation absorptivity of the front of the photovoltaic panel, and the unit area of ​​the photovoltaic panel. The light transmittance of snow is a constant. The base is the product of the negative extinction coefficient of the snow and the real-time total thickness of the snow. The heat flux through contact conduction between the photovoltaic panel and the underlying snow is equal to the product of the interfacial heat transfer coefficient between the photovoltaic panel and the snow, the temperature difference between the photovoltaic panel and the snow, and the dynamic reduction factor. The dynamic reduction factor is set to 1.0 during the snow phase change stage and 0.3 during the dry snow stage. The interfacial heat transfer coefficient between the photovoltaic panel and the snow is equal to 2 multiplied by the dynamic thermal conductivity of the snow and then divided by the real-time total thickness of the snow. The power generation of photovoltaic panels is calculated using the following formula: ， In the formula, The amount of electricity generated by the photovoltaic panels; The solar radiation heat flux absorbed by the front of the photovoltaic panel; The heat flux of solar radiation reflected from the ground absorbed by the back of the photovoltaic panel; The photoelectric conversion efficiency of the photovoltaic panel under standard test conditions; The power temperature coefficient; Temperature of the photovoltaic panel; All heat flux and power generation are calculated based on the unit area of ​​the roof, and the sign of each item is positive when energy flows into the photovoltaic panel and negative when it flows out. The rate of change of heat storage of the photovoltaic panel with snow accumulation is equal to the product of the mass of the photovoltaic panel and its specific heat capacity, multiplied by the rate of change of its surface temperature over time.

6. The method according to claim 5, characterized in that: The real-time total thickness of the snow is determined based on the equivalent density of the snow in the snow freeze-thaw cycle. The equivalent density of the snow is equal to the sum of the real-time mass of dry snow, ice and liquid water in the snow layer, divided by the product of the real-time total thickness of the snow and the unit area of ​​the roof. The dynamic thermal conductivity of snow is calculated using the following formula: ， ， In the formula, The dynamic thermal conductivity of the snow is given. The dynamic equivalent weight of snow cover; This represents the real-time equivalent density during the snow freeze-thaw cycle.

7. The method according to claim 1, characterized in that: The energy conservation model for snow cover is expressed as follows: the net heat flux absorbed by the snow cover is equal to the product of the snow mass and its specific heat capacity multiplied by the rate of change of its temperature over time; the rate of change of heat storage is equal to the algebraic sum of the solar radiation heat flux absorbed by the upper surface of the snow cover and the contact conduction heat flux between the photovoltaic panel and the bottom snow cover, minus the atmospheric convection heat transfer flux between the upper surface of the snow cover and the surrounding air and the long-wave radiation heat transfer flux from the upper surface of the snow cover to the sky.

8. The method according to claim 1, characterized in that: The mass conservation model for snow accumulation during the freeze-thaw cycle is as follows: The change in liquid water mass is equal to the mass of snow melted during the current calculation time step minus the mass of ice formed and the mass of lost meltwater runoff. The sum of the changes in the mass of dry snow and ice equals the mass of ice formed in the current calculation time step minus the mass of melted snow. The sum of the changes in the mass of liquid water, dry snow, and ice is equal to the negative of the mass of meltwater runoff lost in the current calculation time step; The mass of snow melted within the current calculation time step is equal to the net heat flux absorbed by the snow within that time step multiplied by the time step length, and then divided by the latent heat of phase change of the snow. The ice mass within the current calculation time step is equal to the absolute value of the net heat flux of snow accumulation within that time step multiplied by the time step length, and then divided by the latent heat of phase change of snow melting. The mass of meltwater runoff lost within the current calculation time step is the meltwater seepage velocity within the snow cover multiplied by the time step. The specific formula for calculating the seepage velocity of meltwater within snow cover is as follows: ， In the formula, The density of water; The dynamic permeability of snow cover; It is the acceleration due to gravity; The installation tilt angle of the photovoltaic panels; The dynamic viscosity of liquid water; This represents the current water saturation level of the snow cover. The irreducible water saturation level is set to 5%. The dynamic permeability of the snow is calculated using the following formula: ， In the formula, For reference permeability, take m²; The real-time porosity of the snow cover; The density of dry snow; This is the density of ice.

9. The method according to claim 1, characterized in that: The cumulative positive energy absorbed by the snow is calculated using the following formula: ， In the formula, The accumulated positive energy absorbed by the snow. To accumulate the positive energy threshold, for Net heat flux absorbed by snow accumulation at all times; when It was determined that the snow had slid off the surface of the photovoltaic panel.

10. A prediction system for the thermal snow melting process and thermoelectric performance of a rooftop photovoltaic system, used to execute the method as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to collect historical meteorological data for the target area, including total solar radiation and outdoor air temperature; The processing module interacts with the acquisition module to preprocess the historical meteorological data, perform cluster analysis on the preprocessed historical meteorological data, extract typical meteorological combination conditions, and combine them with various snow thicknesses to form background meteorological conditions. The model building module interacts with the processing module to construct multi-node coupled heat transfer models for snowy and snowless stages. The multi-node coupled heat transfer model for the snowy stage includes an energy conservation model for the roof, photovoltaic panels, and snow accumulation, as well as a mass conservation model for the snow accumulation during the freeze-thaw cycle. The multi-node coupled heat transfer model for the snowless stage includes an energy conservation model for the roof and an energy conservation model for the photovoltaic panels. The dynamic simulation and prediction module interacts with the model building module to input the background meteorological conditions as boundary conditions into the multi-node coupled heat transfer model of the snowy stage, calculate the transient temperature and cumulative positive energy of the snow, and divide the snow melting process into the dry snow stage, the snow phase change stage and the snowless stage according to the preset temperature threshold and cumulative positive energy threshold. The dry snow stage and the snow phase change stage call the multi-node coupled heat transfer model of the snow-covered stage for dynamic simulation and prediction. The snowless stage switches to the multi-node coupled heat transfer model of the snowless stage for dynamic simulation and prediction. Each stage is connected in sequence, and the corresponding model is switched according to the real-time judgment result of the snow status until the snow is completely melted. Based on the above dynamic simulation and prediction results, the photovoltaic power generation and the time for complete snow melting under different background meteorological conditions were obtained.

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