Solar irradiance prediction method and system based on physical process

Abstract

The invention discloses a solar irradiance prediction method and system based on a physical process, and the method comprises the steps: obtaining the longitude and latitude and the altitude of a target region, and calculating the physical parameters of the current sun in combination with the current product day and the time of the east eight regions; according to the humidity and the temperature of the current environment, the current linker turbidity factor is adjusted in combination with the physical parameters; constructing a horizontal irradiance prediction physical model; and solving the horizontal irradiance prediction physical model based on a current physical parameter and a linker turbidity factor to obtain a real-time prediction result of the solar irradiance of the horizontal plane in the target area. According to the model, the dynamic Linker turbidity factor which is adjusted in real time is utilized, the requirement for input parameters is greatly reduced, high prediction precision and stability are achieved, the model is suitable for various weather conditions, the requirement for ultra-short-term photovoltaic power output prediction can be met, and important support is provided for optimization of photovoltaic system operation and power grid dispatching.

Description

Technical Field

[0001] The present invention relates to the technical field of irradiance prediction, and particularly to a solar irradiance prediction method and system based on physical processes. Background Art

[0002] As a renewable energy source with wide distribution and huge reserves, solar energy has become an important direction in the energy transition. However, photovoltaic power generation has the characteristics of volatility, intermittency, and randomness. Accurate and reliable prediction of photovoltaic output has become a key technology urgently needed to solve the problem. Since solar irradiance is the core driving factor of a photovoltaic power generation system, accurate prediction of solar irradiance can directly improve the accuracy of photovoltaic output prediction, optimize the operation strategy of the power grid, and ensure the stability and reliability of the photovoltaic power generation system.

[0003] In the field of irradiance prediction technology, compared with empirical models and data-driven models, physical models are based on the transmission and attenuation process of solar radiation in the atmosphere, considering the influence of factors such as atmospheric composition, cloud cover, and aerosols on solar irradiance. They can accurately simulate the physical process of solar radiation in the atmosphere, are applicable to various climate conditions and geographical environments, and can better meet the requirements of ultra-short-term photovoltaic output prediction. However, although the current existing physical models can already explain the variation law of solar radiation and have a certain accuracy, they often require a large number of input parameters, have extremely high requirements for the quality of meteorological data, and the prediction process is cumbersome and complex.

[0004] The Linke Turbidity Coefficient is a parameter used to describe atmospheric transparency. It reflects the scattering and absorption effects of aerosols, water vapor, and other impurities in the atmosphere on solar radiation. Introducing it enables the physical model of irradiance prediction to better adapt to different atmospheric conditions, thereby improving the accuracy and application range of the model. However, the Linke Turbidity Coefficient of traditional models is an empirical coefficient. Existing research usually estimates it based on the Linke constant or through ground observation data or satellite data, which has extremely high requirements for the accuracy of measurement instruments, and the stability of the dynamic Linke Turbidity Coefficient obtained under extreme weather and rapidly changing climate conditions is poor, unable to meet the requirements of ultra-short-term photovoltaic output prediction. Summary of the Invention

[0005] To address the deficiencies in the existing technology, the present invention provides a method and system for predicting solar irradiance based on physical processes. By establishing a physical model for predicting horizontal irradiance based on dynamic compensation of air mass and seasonal cycle compensation through a more complete physical process, and a dynamically adjusted Linke turbidity factor based on real-time physical processes, the present invention greatly reduces the requirements for input parameters, has high prediction accuracy and stability, is applicable to various climate conditions, can meet the requirements of ultra-short-term photovoltaic power output prediction, and provides important support for optimizing the operation of photovoltaic systems and grid dispatching.

[0006] The present invention adopts the following technical solutions.

[0007] In a first aspect, the present invention provides a method for predicting solar irradiance based on physical processes, the method comprising: Obtaining the longitude, latitude and altitude of the target area, and calculating the physical parameters of the current sun in combination with the current day of the year and the time in the eighth time zone; Adjusting the current Linke turbidity factor according to the humidity and temperature of the current environment in combination with the physical parameters; Constructing a physical model for predicting horizontal irradiance; Solving the physical model for predicting horizontal irradiance based on the current physical parameters and the Linke turbidity factor to obtain a real-time prediction result of the horizontal plane solar irradiance in the target area.

[0008] Optionally, the physical parameters include: solar altitude angle , air mass and solar radiation intensity at the top of the atmosphere .

[0009] Optionally, the expression of the Linke turbidity factor is as follows:

[0010] In the formula, represents the Linke turbidity factor; optical thickness of clean and dry atmosphere; represents the optical thickness of water vapor; represents the optical thickness of aerosol.

[0011] Optionally, the calculation formulas for the optical thickness of clean and dry atmosphere, the optical thickness of water vapor and the optical thickness of aerosol are as follows respectively:

[0012]

[0013]

[0014] In the formula, Represents the air quality; Represents the solar altitude angle; And Represent the humidity and temperature of the environment respectively.

[0015] Optionally, the expression of the horizontal irradiance prediction physical model is as follows:

[0016] In the formula, Represents the solar irradiance intensity on the horizontal plane; Represents the solar altitude angle; Represents the solar radiation intensity at the top of the atmosphere; Represents the air mass; Are the first and second altitude correlation coefficients respectively; Represents the Linke turbidity factor; Is the correction factor.

[0017] Optionally, the calculation formula of the correction factor is as follows:

[0018] In the formula, Is the correction factor, Is the dynamic compensation term of the air mass Regression coefficient; Is the seasonal cycle compensation term Regression coefficient; Is the typical value of the air mass; Is the currently calculated air mass; Is the seasonal phase reference date; Is the current day of the year, that is, the number of days continuously accumulated from January 1st of the current year to the current date.

[0019] Optionally, the regression coefficients And Are obtained through the following steps: Collect the measured irradiance data of different reference radiation observation BSRN sites and construct a residual data set; Based on the residual data set, use the L-M algorithm to fit and optimize the regression coefficients of the correction factor In the horizontal irradiance prediction physical model, and finally output the optimal And Fitting results.

[0020] In a second aspect, the present invention provides a solar irradiance prediction system based on a physical process, which operates according to the steps of any one of the methods in the first aspect of the present invention. The system includes: An acquisition module, configured to acquire the longitude, latitude and altitude of a target area, and calculate the physical parameters of the current sun by combining the current day of year and the time in the eighth time zone east of Greenwich; An adjustment module, configured to adjust the current Linke turbidity factor according to the humidity and temperature of the current environment and in combination with the physical parameters; A construction module, configured to construct a physical model for predicting horizontal irradiance; A prediction module, configured to solve the physical model for predicting horizontal irradiance based on the current physical parameters and the Linke turbidity factor, and obtain a real-time prediction result of the horizontal plane solar irradiance in the target area.

[0021] In a third aspect, the present invention provides a terminal, including a processor and a storage medium; The storage medium is used for storing instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of the first aspect of the present invention.

[0022] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method according to any one of the first aspect of the present invention are implemented.

[0023] The beneficial effects of the present invention are that, compared with the prior art, 1. For the existing physical model for predicting horizontal irradiance based on the Linke turbidity factor, either a large amount of historical data is used to obtain a static Linke turbidity factor, which is actually difficult to apply in modern scenarios with rapidly changing weather; or an empirical dynamic Linke turbidity factor is obtained by combining historical data and high-precision meteorological observation instruments, which has poor stability under complex and extreme weather conditions and cannot meet the requirements of ultra-short-term photovoltaic power prediction. The present invention constructs a new dynamic numerical calculation method of the Linke turbidity factor by combining two basic meteorological parameters, temperature and humidity, and physical processes. Without relying on a large amount of historical data and high-precision meteorological observation instruments, even under complex and extreme weather conditions, a stable Linke turbidity factor can be obtained based on physical processes to predict horizontal irradiance, showing higher stability in various meteorological conditions.

[0024] 2. The horizontal irradiance prediction physical model of the present invention, for low air mass regions, by adopting the aerosol scattering enhancement factor and the sine periodic function based on the day of the year n, designs the dynamic air mass compensation term and the seasonal cycle compensation term, solves the seasonal difference and the amplification of the noon deviation of the static Linke turbidity factor model, constructs the horizontal irradiance prediction physical model adapted to the dynamic Linke turbidity factor obtained based on the physical process of the present invention based on a more perfect physical process, is more capable of coping with rapidly changing extreme weather, gets rid of the dependence on precise meteorological measuring instruments, greatly reduces the demand for input parameters, and has excellent prediction accuracy and stability, is applicable to various climate conditions, can meet the requirements of ultra-short-term photovoltaic power output prediction, and provides important support for optimizing the operation of photovoltaic systems and grid dispatching.

[0025] 3. The experimental result data obtained by comparing various performance indicators of the horizontal irradiance prediction physical model provided by the present invention include: the mean bias error (MBE) is 0.1441, the mean absolute error (MAE) is 4.3513, and the coefficient of determination (R²) is 0.9818, which has higher prediction accuracy and stability compared with traditional models; in addition, the physical process-based model of the present invention will also show better potential and prospects in the combined application with prediction methods such as machine learning and data-driven. Description of the Drawings

[0026] Figure 1 is a schematic diagram of the physical process-based solar irradiance prediction process in an embodiment of the present invention; Figure 2 is a schematic diagram of the calculation process of the horizontal irradiance G in an embodiment of the present invention; Figure 3 is the frequency histogram and box plot of the Linke turbidity factor in an embodiment of the present invention; Figure 4 is a comparison schematic diagram of the predicted irradiance value and the measured irradiance within a day in an embodiment of the present invention; Figure 5 is a scatter density schematic diagram of the predicted irradiance value and the measured irradiance of the model, Ineichen model, and Kasten model constructed in the research of an embodiment of the present invention; Figure 6 is the structural principle block diagram of the physical process-based solar irradiance prediction system in an embodiment of the present invention. Detailed Embodiments

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in the present invention are only a part of the embodiments of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0028] Embodiment 1: Referring to Figure 1 , an embodiment of the present invention provides a method for predicting solar irradiance based on physical processes, specifically including the following steps: Step 1: Obtain the longitude, latitude, and altitude of the target area, and calculate the physical parameters of the current sun in combination with the current day of the year and the time in the eighth time zone. The physical parameters calculated in this embodiment include the solar declination angle , the solar hour angle , the solar altitude angle , the air mass , and the solar radiation intensity at the top of the atmosphere . The specific calculation process is as follows: (1) The solar declination angle determines the latitude position of the direct solar point, which reflects the influence of the inclination angle of the earth's revolution orbit on the distribution of solar radiation and affects the solar radiation intensity and sunshine duration in different seasons:

[0029] In the formula, is the solar declination angle; is the day of the year arranged in chronological order, that is, the number of days continuously accumulated from January 1st of the current year to the current date.

[0030] (2) The solar hour angle describes the position of the sun relative to the local meridian. It reflects the movement trajectory of the sun in a day and can be used to calculate the solar altitude angle. It is a key parameter for short-term irradiance prediction. The calculation formula based on the time in the eighth time zone is:

[0031] In the formula, is the time in the eighth time zone; is the solar hour angle; is the longitude of the target area.

[0032] (3) The solar altitude angle Determines the angle between the sun's rays and the Earth's surface. The larger the altitude angle, the closer the sun's rays are to being perpendicular, and the more solar radiation energy is received per unit area; the smaller the altitude angle, the longer the path of the sun's rays through the atmosphere, the stronger the scattering and absorption of solar radiation by the atmosphere, and the weaker the direct radiation reaching the ground. Based on the above, the solar declination angle , the solar hour angle , the calculation method of the solar altitude angle is as follows:

[0033] In the formula, is the latitude of the target area; is the solar altitude angle.

[0034] (4) Air mass is a key parameter describing the attenuation degree of solar radiation in the atmosphere. As the air mass increases, the distance that the sun's rays travel in the atmosphere becomes longer, the scattering and absorption effects increase, and the direct radiation reaching the ground weakens. Its calculation method is:

[0035] In the formula, is the air mass of the target area.

[0036] (5) Solar radiation intensity at the top of the atmosphere :

[0037]

[0038] In the formula, is the solar radiation intensity at the top of the atmosphere; is the solar constant, = 1367 W / m2; is the deviation correction factor, which is used to correct the deviation caused by the change in the distance between the sun and the Earth.

[0039] Step 2: According to the humidity and temperature of the current environment, adjust the current Linke turbidity factor in combination with the physical parameters; In this embodiment, the Linke turbidity factor is used to measure the turbidity degree of the actual atmosphere relative to the clean and dry atmosphere. The specific quantification process of the Linke turbidity factor is as follows: Step 2.1: Accurately calculate the optical thickness of the clean and dry atmosphere through the air mass obtained in Step 1. Its calculation formula is as follows: ; Step 2.2: Calculate the precipitable water based on the currently obtained environmental humidity and temperature, and use the precipitable water to calculate the water vapor optical depth .

[0040] Calculate the precipitable water from humidity and temperature , and the calculation formula is as follows:

[0041] In the formula, is the humidity; is the temperature.

[0042] Use the precipitable water to calculate the water vapor optical depth , and the formula is as follows:

[0043] In the formula, is the precipitable water.

[0044] Integrate the above two formulas to obtain the water vapor optical depth The final expression is as follows: .

[0045] Step 2.3: Represent the aerosol transmittance through the aerosol coefficient and the atmospheric mass obtained in Step 1 , and combined with the Bouguer-Lambert-Beer law, the calculation formula for the aerosol optical depth can be obtained:

[0046] In the formula, is the aerosol transparency coefficient, which can be obtained from the following formula:

[0047] Integrate the above two formulas to obtain the expression of the aerosol optical depth as follows:

[0048] Step 2.4: Relate the Linke turbidity factor to the total atmospheric optical depth, and calculate the stable dynamic Linke turbidity factor based on physical processes through the optical depths contributed by different components obtained in Steps 2.1 - 2.3. The formula is as follows:

[0049] In the formula, is the Linke turbidity factor; is the optical depth of the clean and dry atmosphere; is the water vapor optical depth; is the aerosol optical depth.

[0050] Step 3: Construct a physical model for predicting horizontal irradiance; Specifically, the expression of the physical model for predicting horizontal irradiance constructed in this embodiment is as follows:

[0051] In the formula, represents the solar irradiance intensity on the horizontal plane; represents the solar altitude angle; represents the solar radiation intensity at the top of the atmosphere; represents the air mass; are the first and second altitude correlation coefficients respectively; represents the Linke turbidity factor; is the correction factor.

[0052] Furthermore, in view of the problem that the dynamic Linke turbidity factor input prediction model in the existing research does not fully compensate for the influence of the annual periodic change of the solar altitude angle on the air mass and underestimates the optical depths of aerosols and water vapor, the aerosol scattering enhancement factor is used as the dynamic air mass compensation term, and a seasonal cycle compensation term is designed according to the sine periodic function based on the day of the year n. The specific correction factor design is as follows:

[0053] In the formula, is the correction factor, is the regression coefficient of the dynamic air mass compensation term ; is the regression coefficient of the seasonal cycle compensation term ; is the typical value of the air mass. In this embodiment, takes the value of 2.5; is the currently calculated air mass; is the seasonal phase reference day. In this embodiment, takes the day of the year 172 (summer solstice) as the seasonal phase reference day; is the current day of the year.

[0054] As a preferred but non-limiting embodiment of the present invention, the values of the regression coefficients and are obtained through the following steps: Collect the measured irradiance data of different reference radiation observation BSRN stations and construct a residual data set; Based on the residual data set, use the L-M (Levenberg-Marquardt) algorithm for the correction factor in the physical model for predicting horizontal irradiance The regression coefficients are fitted and optimized, and finally the optimal and fitting results are output.

[0055] Preferably, the final fitting results of the regression coefficients in this embodiment are: = 0.0503, = 0.0446.

[0056] Correspondingly, a physical model for predicting the horizontal irradiance adapted to the dynamic Linke turbidity factor of the present invention is constructed according to the correction factor as follows:

[0057] In the formula, represents the solar irradiance intensity on the horizontal plane; represents the solar altitude angle; represents the solar radiation intensity at the top of the atmosphere; represents the air mass; represents the Linke turbidity factor, is the current day of the year; are the first and second altitude correlation coefficients respectively, and their calculation methods are respectively:

[0058]

[0059] In the formula, is the altitude of the target area.

[0060] Step 4: Based on the current physical parameters and the Linke turbidity factor, solve the physical model for predicting the horizontal irradiance to obtain the real-time prediction result of the solar irradiance on the horizontal plane in the target area.

[0061] As Figure 2 shown, in the physical model for predicting the horizontal irradiance constructed by the present invention, in addition to the solar physical parameters describing the physical process, only two basic meteorological data of humidity ( ) and temperature ( ) need to be input, and the dynamic Linke turbidity factor ( ) can be calculated to describe the atmospheric transparency; by using the simple input characteristics of this model, the prediction of the horizontal irradiance can be carried out without complex meteorological observation equipment; at the same time, due to being based on the physical process, this model not only has high precision, but also has higher stability in most meteorological conditions and can better respond to rapidly changing extreme weather; as a basic physical model, this model has excellent accuracy and stability, and will also show better potential and prospects in the combined application with prediction methods such as machine learning and data-driven.

[0062] It is further explained that the prediction models in existing studies, such as the horizontal surface irradiance model based on Link turbidity factor developed by Kasten (referred to as Kasten model), have the advantage of integrating solar geometric parameters with atmospheric optical attenuation to achieve a rapid estimation of the total horizontal surface irradiance, and can adjust the atmospheric turbidity and site elevation. However, due to the limitation of static T L The model is difficult to apply in modern scenarios with rapidly changing weather. Therefore, Pierre Ineichen developed a general-purpose water surface irradiance model (Ineichen model) based on the Kasten model, which uses dynamic Link turbidity factor input. Although the model has the ability to input dynamic Link turbidity factor data and eliminates the dependence on solar geometry to a certain extent, the dynamic Link turbidity factor used in the model is based on historical observation data. The model does not fully compensate for the annual periodic change of the solar altitude angle on the atmospheric mass m a , and underestimated the optical depth of aerosols and water vapor, resulting in certain seasonal differences in the final irradiance prediction results and amplified midday deviations.

[0063] In order to solve the above problems, the present invention is based on a new calculation method of Link turbidity factor. Aiming at the problem that the dynamic Link turbidity factor input prediction model in the existing research does not fully compensate the impact of the annual periodic change of the solar altitude angle on the air quality, and underestimates the optical thickness of aerosol and water vapor, an aerosol scattering enhancement factor is used as the dynamic compensation term for air quality, and a seasonal cycle compensation term is designed according to a sinusoidal periodic function based on the cumulative day n. The BSRN irradiance measured data of different stations are used to construct a residual data set, and the Levenberg-Marquardt algorithm is used to solve the optimal parameters. The calculation of the correction factor is completed through iterative optimization. Finally, a dynamic Link turbidity factor T adapted to the present invention is developed according to the correction factor. L Physical model for predicting horizontal irradiance.

[0064] The present invention is based on the Link turbidity factor T L A new dynamic numerical calculation method is used to construct a physical model for horizontal irradiance prediction that is suitable for the dynamic turbidity factor of the present invention. The model has few input parameters and is suitable for a variety of climatic conditions and geographical environments. It solves the problems of seasonal differences and noon deviation amplification in the existing dynamic Link turbidity factor model, and gets rid of the dependence on precise meteorological measuring instruments. The model has better stability and application in rapidly changing extreme weather.

[0065] In order to verify the dynamic Link turbidity factor T calculated based on the physical process L The reliability and stability of the present invention are based on the Link turbidity factor T in the simulation software. LThe calculation model uses the full-year data of the Baseline Surface Radiation Network (BSRN) Xianghe Station in 2014 and the corresponding meteorological data as inputs, and plots the obtained dynamic Linke turbidity factors as frequency histograms and box plots, as Figure 3 shown.

[0066] As Figure 3 shown, the distribution of the Linke turbidity factors in the histogram shows a right-skewed state. Most of the data are concentrated between 1.2 and 1.6, and the highest frequency point is approximately around 1.3, indicating that the atmospheric turbidity in the study area is usually within a certain level range, and extreme turbidity situations rarely occur. The median and interquartile range (IQR) in the box plot show the central tendency and dispersion degree of the data. The median is approximately around 1.4, and the IQR is between 1.2 and 1.6, which is close to the standard Linke turbidity factor range of this station, and the data is relatively concentrated. Although there are a small number of outliers, the overall shows excellent dynamic stability.

[0067] The Baseline Surface Radiation Network (BSRN) is a set of high-quality irradiance measurement values suitable for verifying the accuracy of the model, and the measurement results are obtained once every minute. The BSRN dataset is screened for outliers according to the method proposed by Roesch, and reliable data is retained. The present invention selects the full-year data of the BSRN Xianghe Station in 2014 as the measured irradiance dataset of the horizontal irradiance, establishes a physical model G for predicting the horizontal irradiance on the simulation software, and calculates the predicted value dataset of the horizontal irradiance according to the corresponding meteorological data as the input; according to the BSRN dataset and the predicted data, a comparison chart of the predicted value and the measured irradiance of the horizontal irradiance on a certain day of that year is drawn, as Figure 4 shown.

[0068] As Figure 4 shown, in the prediction of the model of the present invention, it can capture the basic change trend of the horizontal irradiance, the coincidence degree between the predicted value and the measured irradiance is relatively high, and the curve trends are basically the same. At each time period, the predicted value can follow the change of the measured irradiance well, and the model has a relatively high accuracy and stability.

[0069] To compare and verify the accuracy of the model, three performance indicators are used to evaluate the accuracy of different models, namely: Mean Bias Error (MBE), Mean Absolute Error (MAE), and Coefficient of Determination (R2); according to the minute-level irradiance dataset of the BSRN Xianghe Station in one year and the prediction results of three different horizontal irradiance models, a scatter density map of the predicted value and the measured irradiance of the horizontal irradiance at this station is drawn, as Figure 5 shown.

[0070] As Figure 5As shown, the color distribution in the figure represents the density of data points. The color changes from purple to red, usually indicating areas from low density to high density. The dashed line in the figure represents the ideal 1:1 fitting line, that is, the case where the model prediction value is exactly equal to the measured irradiance. The thin red line represents the fitting result of the data. It can be seen that the fitting result of the model almost completely coincides with the ideal 1:1 fitting line, intuitively reflecting the overall accuracy of the model. In the low irradiance region, the data points are concentrated near the 1:1 fitting line, and an almost perfect overall fit is observed between the predicted value and the measured irradiance. In the high irradiance region, the distribution of data points is slightly scattered, but noting the density distribution of data points, most data points are still concentrated near the 1:1 fitting line, with an overall good fit. Compared with other models, the model of the present invention has a lower MAE and a higher R2.

[0071] The model accuracy is more accurately evaluated according to performance indicators respectively: the mean bias error (MBE) is 0.1441, indicating that the model prediction value is on average 0.1441 units higher than the actual measured irradiance, with almost no systematic bias; the mean absolute error (MAE) provides an unbiased error measure of 4.3513%, indicating that the average absolute error between the model prediction value and the actual measured irradiance accounts for a relatively low percentage of the measured irradiance, and the prediction accuracy of the model is relatively high; the coefficient of determination (R2) is 0.9818, indicating that the model can explain 98.12% of the variability of the observed data, showing that the model fits the data very well, and there is a strong linear relationship between the model prediction value and the actual measured irradiance.

[0072] Table 1

[0073] As shown in Table 1, compared with the Kasten model and the Ineichen model in existing research, the model constructed by the present invention shows a lower mean absolute error and a higher coefficient of determination, demonstrating higher accuracy and more excellent stability.

[0074] Through the evaluation of multiple performance indicators and comparison with existing traditional models, the conclusion is obtained: the model of the present invention has high accuracy in predicting horizontal irradiance, with small prediction errors, and the model can well explain the variability of the observed data, and can meet the requirements of ultra-short-term photovoltaic power output prediction.

[0075] The beneficial effect of the present invention is that, compared with the prior art, 1. For the existing physical models for predicting horizontal irradiance based on Linke turbidity factor, either a large amount of historical data is used to obtain the static Linke turbidity factor, which is actually difficult to apply in modern scenarios with rapidly changing weather; or an empirical dynamic Linke turbidity factor is obtained by combining historical data and high-precision meteorological observation instruments, but its stability is poor under complex and extreme weather conditions and cannot meet the requirements of ultra-short-term photovoltaic power prediction. The present invention constructs a new dynamic numerical calculation method of the Linke turbidity factor by combining two basic meteorological parameters, temperature and humidity, with physical processes. Without relying on a large amount of historical data and high-precision meteorological observation instruments, even under complex and extreme weather conditions, a stable Linke turbidity factor can be obtained based on physical processes to predict horizontal irradiance, showing higher stability in various meteorological conditions.

[0076] 2. The horizontal irradiance prediction physical model of the present invention, for low atmospheric mass regions, designs an atmospheric mass dynamic compensation term and a seasonal cycle compensation term by adopting an aerosol scattering enhancement factor and a sine periodic function based on the day of the year n, solves the problems of seasonal differences and amplified noon deviations of the static Linke turbidity factor model, and constructs a horizontal irradiance prediction physical model adapted to the dynamic Linke turbidity factor obtained based on physical processes of the present invention based on a more complete physical process. It can better respond to rapidly changing extreme weather, get rid of the dependence on precise meteorological measurement instruments, greatly reduce the demand for input parameters, and has excellent prediction accuracy and stability. It is applicable to various climate conditions and can meet the requirements of ultra-short-term photovoltaic power output prediction, providing important support for optimizing the operation of photovoltaic systems and power grid dispatching.

[0077] 3. The experimental result data obtained by comparing various performance indicators of the horizontal irradiance prediction physical model provided by the present invention include: the mean bias error (MBE) is 0.1441, the mean absolute error (MAE) is 4.3513, and the coefficient of determination (R²) is 0.9818, showing higher prediction accuracy and stability compared with traditional models; in addition, the physical process-based model of the present invention will also show better potential and prospects in the combined application with prediction methods such as machine learning and data-driven.

[0078] Example Two: As Figure 6 shown, the present invention provides a physical process-based solar irradiance prediction system. The system is used to implement the steps of the method in the first embodiment above. Specifically, the system includes: An acquisition module, configured to acquire the longitude, latitude and altitude of the target area, and calculate the physical parameters of the current sun in combination with the current day of the year and the time in the eighth time zone; An adjustment module, configured to adjust the current Linke turbidity factor according to the humidity and temperature of the current environment in combination with the physical parameters; A building block for building a physical model for horizontal irradiance prediction; A prediction module for solving the physical model for horizontal irradiance prediction based on current physical parameters and the Linke turbidity factor to obtain a real-time prediction result of the horizontal solar irradiance in the target area.

[0079] The solar irradiance prediction system based on physical processes provided by the embodiments of the present invention and the solar irradiance prediction method based on physical processes provided in Embodiment 1 are based on the same technical concept, and can produce beneficial effects as described in Embodiment 1. Contents not described in detail in this embodiment can be referred to in Embodiment 1.

[0080] Embodiment 3: A terminal provided by an embodiment of the present invention includes a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the method according to any one of Embodiment 1.

[0081] Embodiment 4: A computer-readable storage medium provided by an embodiment of the present invention stores a computer program, and when the program is executed by a processor, it implements the steps of the method according to any one of Embodiment 1.

[0082] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions thereon for causing a processor to implement various aspects of the present disclosure.

[0083] The computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or raised structures in grooves storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as an instantaneous signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0084] The computer-readable program instructions described herein can be downloaded to various computing / processing devices from a computer-readable storage medium or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0085] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A solar irradiance prediction method based on physical processes, characterized in that: Methods include: Obtain the latitude, longitude and altitude of the target area, and calculate the current physical parameters of the sun in combination with the current accumulated day and GMT+8 time; According to the humidity and temperature of the current environment, the current Link turbidity factor is adjusted in combination with the physical parameters; Construct a physical model for horizontal irradiance prediction; Based on the current physical parameters and Link turbidity factor, the horizontal irradiance prediction physical model is solved to obtain the real-time prediction result of the horizontal surface solar irradiance in the target area.

2. The solar irradiance prediction method based on physical process according to claim 1 is characterized in that: The physical parameters include: solar altitude angle , air quality and the solar radiation intensity at the upper boundary of the atmosphere .

3. The solar irradiance prediction method based on physical process according to claim 1 is characterized in that: The Link turbidity factor is expressed as follows: In the formula, represents the Link turbidity factor; The optical thickness of the clean dry atmosphere; represents the water vapor optical thickness; represents the aerosol optical depth.

4. The solar irradiance prediction method based on physical process according to claim 3 is characterized in that: The calculation formulas for the clean dry atmosphere optical thickness, water vapor optical thickness and aerosol optical thickness are as follows: In the formula, Indicates the quality of the atmosphere; represents the solar altitude angle; and Respectively represent the humidity and temperature of the environment.

5. The solar irradiance prediction method based on physical process according to claim 1, characterized in that: The expression of the horizontal irradiance prediction physical model is as follows: In the formula, Indicates the solar radiation intensity on the horizontal surface; represents the solar altitude angle; Indicates the intensity of solar radiation at the upper boundary of the atmosphere; Indicates the quality of the atmosphere; are the first and second height correlation coefficients, respectively; represents the Link turbidity factor; is the correction factor.

6. The solar irradiance prediction method based on physical process according to claim 5 is characterized in that: The calculation formula of the correction factor is as follows: In the formula, is the correction factor, is the atmospheric mass dynamic compensation term The regression coefficient of Compensation for seasonal cycle The regression coefficient of It is a typical value of air quality; is the currently calculated atmospheric mass; It is the seasonal phase reference day; The current accumulated day, that is, the number of consecutive days from January 1 of the current year to the current date.

7. The solar irradiance prediction method based on physical process according to claim 6, characterized in that: The regression coefficient and The value of is obtained by following the steps below: Collect measured irradiance data from different benchmark radiation observation BSRN sites and construct residual data sets; Based on the residual data set, the LM algorithm is used to predict the correction factor in the physical model of horizontal irradiance. The regression coefficient is optimized and the optimal and The fitting results of .

8. A solar irradiance prediction system based on physical processes, running the solar irradiance prediction method based on physical processes as claimed in any one of claims 1 to 7, characterized in that: The system includes: The acquisition module is used to obtain the latitude, longitude and altitude of the target area, and calculate the current physical parameters of the sun in combination with the current accumulated day and the Eastern Time Zone 8; An adjustment module, used to adjust the current Link turbidity factor according to the humidity and temperature of the current environment in combination with the physical parameters; A building module for constructing a physical model for horizontal irradiance prediction; The prediction module is used to solve the horizontal irradiance prediction physical model based on the current physical parameters and Link turbidity factor to obtain the real-time prediction result of the horizontal surface solar irradiance in the target area.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images