A method and system for predicting power generation of a photovoltaic power plant

Abstract

A kind of power generation power prediction method and system of photovoltaic power station, method first calculates the comprehensive efficiency correction coefficient of photovoltaic power station, including photovoltaic module type, photovoltaic array's inclination and azimuth, inverter efficiency, line loss, other factors etc., then based on environment and climate, construct cloud layer movement model to evaluate ground irradiance, and predict ultra-short-term power, then based on historical power generation power data and weather, construct short-term photovoltaic power generation power prediction model, and predict short-term power, finally based on comprehensive efficiency correction coefficient, ultra-short-term photovoltaic power generation power, short-term period photovoltaic power generation power, realize the photovoltaic power generation power prediction of future period;The present application realizes the fine prediction of photovoltaic power station power generation power by comprehensively considering the influence of installation, loss and other factors of photovoltaic power station on power generation power, and combining cloud layer movement, environmental and other climate conditions, and comprehensive historical data analysis, improves the timeliness and accuracy of prediction.

Description

Technical Field

[0001] The present invention relates to a power prediction method, belongs to the field of photovoltaic technology, and in particular to a method and system for predicting the generated power of a photovoltaic power station. Background Art

[0002] With the gradual reduction of oil production and the increasing severity of ecological and environmental problems, people have begun to seek sustainable and clean energy solutions, and solar photovoltaic power generation, as a clean and renewable energy form, has received widespread attention; photovoltaic power stations are affected by a variety of factors during the power generation process, and these factors work together to affect the power generation, causing it to fluctuate; specifically, photovoltaic modules are affected by the intensity of solar radiation and meteorological conditions, and are highly random, volatile and intermittent. Their output voltage and current are directly affected by changes in solar radiation intensity and temperature, showing dynamic response characteristics, and the photoelectric conversion efficiency of photovoltaic modules, the deviation between the nominal power and the actual power of the modules, the light incidence rate, and the initial light-induced degradation effect of the modules. Factors will have an impact on power generation efficiency; in addition, due to the limitations of the manufacturing and installation process, even photovoltaic modules of the same model may have manufacturing errors and it is difficult to ensure that they are at the optimal angle during installation, which further increases the instability of power generation. At the same time, the static and dynamic tracking losses within the photovoltaic power generation system, the impedance loss of cable power transmission, etc. will comprehensively affect the photovoltaic power generation efficiency; however, in the existing technology, the prediction of efficiency is usually obtained by actually testing the conversion efficiency between the power generation and light exposure of the photovoltaic power station, and the influence of other factors is not fully considered. It has certain limitations, and this method is prone to error accumulation, resulting in a large deviation between the future prediction results and the actual results and low accuracy. Therefore, there is an urgent need for a means to solve the above-mentioned defects in the existing technology. Summary of the Invention

[0003] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art and to provide a method and system for predicting the generated power of a photovoltaic power station with high prediction accuracy.

[0004] To achieve the above objectives, the technical solution of the present invention is: a method for predicting the power generation of a photovoltaic power station, comprising:

[0005] S1. Calculate the comprehensive efficiency correction coefficient of the photovoltaic power station. The calculation formula of the comprehensive efficiency correction coefficient is as follows:

[0006] K=K1×K2×K3×K4×...×Kn;

[0007] Among them: k is the comprehensive efficiency correction factor of the photovoltaic power station, K1 is the photovoltaic module type correction factor, K2 is the photovoltaic array inclination and azimuth correction factor, K3 is the inverter efficiency correction factor, K4 is the line loss correction factor, and Kn is the correction factor of other influencing factors;

[0008] S2. Based on the environmental and climatic factors of the photovoltaic power station area, a cloud motion model is constructed using machine learning methods. The cloud motion model is then used to simulate cloud motion to assess ground irradiation intensity and predict ultra-short-term photovoltaic power generation.

[0009] S3. Based on the historical power generation data and weather information of the photovoltaic power station, a short-term photovoltaic power generation prediction model is constructed using a machine learning method. Then, the short-term photovoltaic power generation prediction model is used to predict the short-term photovoltaic power generation;

[0010] S4. Based on the comprehensive efficiency correction coefficient of the photovoltaic power station, the ultra-short-term photovoltaic power generation power, and the short-term photovoltaic power generation power, the photovoltaic power generation power of the photovoltaic power station in a certain future period is predicted. The expression for predicting the photovoltaic power generation power in a certain future period is as follows:

[0011] P_cycle1=K×(alpha×P_ultrashort+(1-alpha)×P_short);

[0012] Among them: P_cyclel is the photovoltaic power generation power in a certain cycle in the future, K is the comprehensive efficiency correction coefficient of the photovoltaic power station, P_ultra_short is the ultra-short-term photovoltaic power generation power, P_short is the short-term photovoltaic power generation power, and alpha is the weight coefficient.

[0013] The method for confirming the correction factor of the photovoltaic module type is as follows:

[0014] S11. Determine the material type of the photovoltaic module and collect relevant technical parameters of the photovoltaic module; the relevant technical parameters include conversion efficiency, maximum output power, open circuit voltage, and short circuit current;

[0015] S12. Based on relevant technical parameters of the photovoltaic module, determine the actual conversion efficiency of the photovoltaic module; the expression of the actual conversion efficiency is as follows:

[0016] η_actual=(Pmax / (Voc*Isc*FF))*100%;

[0017] Where: η_actual is the actual conversion efficiency of the photovoltaic module, Pmax is the maximum output power, Voc is the open circuit voltage, Isc is the short circuit current, and FF is the fill factor;

[0018] S13, confirming the rated conversion efficiency of the photovoltaic module under the standard test condition; the expression of the rated conversion efficiency is as follows:

[0019] η_rated=(Pmax / (B*A))*100%;

[0020] Wherein: η_rated is the rated conversion efficiency of the photovoltaic module, B is the incident light power, and A is the area of the photovoltaic module;

[0021] S14, confirming the photovoltaic module type correction coefficient based on the actual conversion efficiency and the rated conversion efficiency of the photovoltaic module; the expression of the photovoltaic module type correction coefficient is as follows:

[0022] K1=(η_actual / η_rated)×(1-D);

[0023] Wherein: D is the life attenuation rate of the photovoltaic module.

[0024] The method for confirming the inclination angle and azimuth angle correction coefficient of the photovoltaic array is as follows:

[0025] S15, confirming the optimal inclination angle and azimuth angle of the photovoltaic array installation position and the inclination angle and azimuth angle in actual installation;

[0026] S16, taking the power generation of the photovoltaic array under the optimal inclination angle and azimuth angle as the reference condition, and calculating the deviation between the power generation of the photovoltaic array under the inclination angle and azimuth angle in actual installation and the reference condition; the expression of the correction coefficient is as follows:

[0027] K2≈angle optimal / angleA ctual ;

[0028] Wherein: angle optimal is the power generation under the optimal inclination angle and azimuth angle, and angle Actual is the power generation under the inclination angle and azimuth angle in actual installation.

[0029] The method for confirming the inverter efficiency correction coefficient is as follows:

[0030] S17, confirming the ideal efficiency data of the inverter, and simultaneously selecting the inverter efficiency value closest to the actual situation according to the actual operation condition of the photovoltaic module to obtain the actual efficiency data;

[0031] S18, calculating the inverter efficiency correction coefficient based on the ideal efficiency data and the actual efficiency data; the expression of the inverter efficiency correction coefficient is as follows:

[0032] K3=K actual / K ideal ;

[0033] Among them: K actual is the actual efficiency data, K ideal For ideal efficiency data.

[0034] The method for confirming the line loss correction factor is as follows:

[0035] S19. Determine the total resistance of the cables based on the resistivity, length, and cross-sectional area of ​​the cables in the photovoltaic module. The total resistance is expressed as follows:

[0036]

[0037] Where: R is the total resistance, ρ is the resistivity of the cable, L is the length, and A is the cross-sectional area;

[0038] S191. Determine the actual resistance loss power based on the total resistance and current passing through the cable. The expression for the resistance loss power is as follows:

[0039] P_loss=I 2 *R;

[0040] Where: P_loss is the actual resistance loss power, I 2 is the current flowing through the cable, and R is the total resistance of the cable;

[0041] S192. Compare the actual resistance loss power with the theoretical resistance loss power to confirm the line loss correction factor.

[0042] The step S2 specifically includes:

[0043] S21. Collect satellite cloud image data in the photovoltaic power station area and use machine learning methods to simulate cloud movement and predict the degree of cloud movement blocking solar radiation, thereby evaluating the solar irradiance on the ground;

[0044] S22. Based on the solar irradiance on the ground and the measured irradiance at the current time, calculate the expected irradiance after the solar irradiance decays in the future; the expression of the expected irradiance is as follows:

[0045]

[0046] Where: E t+Δt is the expected irradiance after time Δt, E t is the measured irradiance at the current time, k is the cloud attenuation coefficient, C cloud is the proportion of area covered by clouds;

[0047] S23. Calculate the ultra-short-term photovoltaic power generation based on the expected irradiance and the conversion efficiency of the photovoltaic panels in the photovoltaic module. The calculation formula for the ultra-short-term photovoltaic power generation is as follows:

[0048] P=η×E t+△t ×A;

[0049] Where: P is the ultra-short-term photovoltaic power generation power, η is the conversion efficiency of the photovoltaic panel, and A is the area of ​​the photovoltaic panel.

[0050] The step S3 specifically includes:

[0051] S31. Collect historical photovoltaic power generation data and weather data of photovoltaic power stations and build a neural network model;

[0052] S32. Use historical photovoltaic power generation data, weather data, time characteristics, and recent power generation data as input features of the neural network model to make predictions and output the short-term photovoltaic power generation power in the future time period.

[0053] In step S4, the expression of the weight coefficient is as follows:

[0054]

[0055] Among them: A ultra_short is the accuracy index of ultra-short-term forecast, T factor is the time range factor, W factor is the uncertainty factor of weather change, A short It is an indicator of the accuracy of short-term forecasts;

[0056] T factor =e -λt ;

[0057] Where: λ is the decay rate, and t is the prediction time range.

[0058] The ultra-short term in the ultra-short term photovoltaic power generation power refers to a time range within 4 hours, and the short term in the short term photovoltaic power generation power refers to a time range within 48 hours.

[0059] A photovoltaic power station power generation prediction system, which is applied to the above method, comprises:

[0060] The correction coefficient calculation module is used to calculate the comprehensive efficiency correction coefficient of the photovoltaic power station; the calculation formula of the comprehensive efficiency correction coefficient is as follows:

[0061] K=K1×K2×K3×K4×...×Kn;

[0062] Among them: K is the comprehensive efficiency correction coefficient of the photovoltaic power station, K1 is the photovoltaic module type correction coefficient, K2 is the photovoltaic array tilt and azimuth correction coefficient, K3 is the inverter efficiency correction coefficient, K4 is the line loss correction coefficient, and Kn is the correction coefficient of other influencing factors;

[0063] The ultra-short-term power prediction module uses machine learning to build a cloud motion model based on environmental and climatic factors in the photovoltaic power station area. It then uses the cloud motion model to simulate cloud motion to assess ground irradiation intensity and predict ultra-short-term photovoltaic power generation.

[0064] The short-term power prediction module is used to build a short-term photovoltaic power generation prediction model based on the historical power generation data of the photovoltaic power station and weather information using machine learning methods. The short-term photovoltaic power generation prediction model is then used to predict the short-term photovoltaic power generation power.

[0065] The photovoltaic power generation module is used to predict the photovoltaic power generation power of a photovoltaic power station within a certain future cycle based on the comprehensive efficiency correction coefficient of the photovoltaic power station, the ultra-short-term photovoltaic power generation power, and the short-term period photovoltaic power generation power. The expression for predicting the photovoltaic power generation power within a certain future cycle is as follows:

[0066] P_cycle1=K×(alpha×P_ultra_short+(1-alpha)×P_short);

[0067] Among them: P_cycle1 is the photovoltaic power generation power in a certain cycle in the future, K is the comprehensive efficiency correction coefficient of the photovoltaic power station, P_ultra_short is the ultra-short-term photovoltaic power generation power, P_short is the short-term photovoltaic power generation power, and alpha is the weight coefficient.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] The present invention provides a method and system for predicting power generation of a photovoltaic power station. The method first calculates a comprehensive efficiency correction coefficient of the photovoltaic power station, which includes correction coefficients for photovoltaic module type, inclination and azimuth of the photovoltaic array, inverter efficiency, line loss, and other influencing factors. Then, based on environmental and climatic factors in the photovoltaic power station area, a cloud movement model is constructed to evaluate ground radiation intensity and predict ultra-short-term photovoltaic power generation. Then, based on historical power generation data and weather information of the photovoltaic power station, a short-term photovoltaic power generation prediction model is constructed to predict short-term periodic photovoltaic power generation. Finally, based on the comprehensive efficiency correction coefficient of the photovoltaic power station, ultra-short-term photovoltaic power generation, and short-term periodic photovoltaic power generation, the photovoltaic power generation of the photovoltaic power station within a certain future period is predicted. In application, the present invention achieves a refined prediction of the power generation of the photovoltaic power station by comprehensively considering the impact of multiple factors on power generation, such as installation of the photovoltaic power station and line loss, and combining climatic conditions such as cloud movement and environment, as well as comprehensive historical data analysis. The prediction result is more consistent with the actual situation and has high accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flow chart of the method steps of the present invention.

[0071] Figure 2 It is a schematic diagram of the system structure of the present invention.

[0072] Figure 3 It is a schematic diagram of the device structure of the present invention.

[0073] In the figure: correction coefficient calculation module 1, ultra-short-term power prediction module 2, short-term power prediction module 3, photovoltaic power generation module 4, processor 5, memory 6, computer program code 61. DETAILED DESCRIPTION

[0074] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0075] Example 1:

[0076] See also Figure 1 , a method for predicting power generation of a photovoltaic power station, comprising:

[0077] S1. Calculate the comprehensive efficiency correction coefficient of the photovoltaic power station;

[0078] In this embodiment, the comprehensive efficiency correction factor takes into account various efficiency losses that may be encountered in the entire process of the photovoltaic power station from receiving solar radiation to converting it into electrical energy, such as inverter efficiency, line loss, component conversion efficiency, etc.; it can be pre-set, for example, a value of 75%-85%; or it can be estimated through calculation.

[0079] In this embodiment, the main factors affecting the photovoltaic power station itself are:

[0080] Photovoltaic module type: Different types of photovoltaic modules have different conversion efficiencies.

[0081] Inverter efficiency: Inverter will have energy loss when converting DC to AC.

[0082] Line loss: There will be a certain loss of current in the transmission process.

[0083] Module conversion efficiency: The ability of the module to convert solar energy into electrical energy.

[0084] Other factors: such as the inclination and azimuth of the array, system availability, light utilization rate, etc.

[0085] The way to calculate the comprehensive efficiency coefficient K is: collect the technical specifications and performance parameters of photovoltaic modules, inverters and other equipment used in the photovoltaic power station; understand the layout of the photovoltaic power station, the inclination and azimuth of the array, and other design parameters; According to the collected data, calculate the correction coefficient of each influencing factor respectively;

[0086] Specifically, the calculation formula of the comprehensive efficiency correction coefficient is as follows:

[0087] K = K1 x K2 x K3 x K4 x... x Kn;

[0088] Where: K is the comprehensive efficiency correction coefficient of the photovoltaic power station, K1 is the photovoltaic module type correction coefficient, K2 is the inclination and azimuth correction coefficient of the photovoltaic array, K3 is the inverter efficiency correction coefficient, K4 is the line loss correction coefficient, and Kn is the correction coefficient of other influencing factors.

[0089] Further, the method for confirming the photovoltaic module type correction coefficient is as follows:

[0090] S11, determine the material type of the photovoltaic module, and collect the relevant technical parameters of the photovoltaic module; The relevant technical parameters include conversion efficiency, maximum output power, open circuit voltage, and short circuit current.

[0091] The material type of the photovoltaic module includes monocrystalline silicon, polycrystalline silicon, amorphous silicon, etc., and different types of modules have different physical properties and performance.

[0092] The relevant technical parameters can be obtained by referring to the technical specifications table of the photovoltaic module or the data manual provided by the manufacturer.

[0093] S12, based on the relevant technical parameters of the photovoltaic module, confirm the actual conversion efficiency of the photovoltaic module; The expression of the actual conversion efficiency is as follows:

[0094] η_actual = (Pmax / (Voc * Isc * FF)) * 100%;

[0095] wherein: η_actual is the actual conversion efficiency of the photovoltaic module, Pmax is the maximum output power, Voc is the open circuit voltage, Isc is the short circuit current, FF is the fill factor;

[0096] The maximum output power Pmax refers to the maximum power that the photovoltaic module can output under STC conditions, with the unit of watt (W); the open circuit voltage Voc refers to the voltage of the photovoltaic module under open circuit condition, with the unit of volt (V); the short circuit current Isc refers to the current of the photovoltaic module under short circuit condition, with the unit of ampere (A); the fill factor FF refers to the ratio of the product of voltage and current at the maximum power point to the product of open circuit voltage and short circuit current, which is a dimensionless value.

[0097] S13, confirming the rated conversion efficiency of the photovoltaic module under standard test conditions; the expression of the rated conversion efficiency is as follows:

[0098] η_rated = (Pmax / (B * A)) * 100%;

[0099] wherein: η_rated is the rated conversion efficiency of the photovoltaic module, B is the incident light power, and A is the area of the photovoltaic module;

[0100] In this embodiment, under standard test conditions (STC), it is assumed that the incident light power is 1000 W / m 2 , and the temperature of the photovoltaic module is set to 25℃. Therefore, if the area of the module (unit: square) is known, the expression of the rated conversion efficiency is as follows:

[0101] η_rated = (Pmax / (1000 * A)) * 100%;

[0102] Considering the decay characteristics of the photovoltaic module during long-term operation, due to various reasons such as material aging and environmental factors, its performance will gradually decay over time. The decay rate of the module can be determined according to the data provided by the manufacturer or industry standards. Finally, combined with the conversion efficiency and decay characteristics of the photovoltaic module, as well as other possible influencing factors (such as dust shielding, environmental temperature changes, etc.), the specific value of the photovoltaic module type correction coefficient is determined.

[0103] S14, confirming the photovoltaic module type correction coefficient based on the actual conversion efficiency and the rated conversion efficiency of the photovoltaic module; the expression of the photovoltaic module type correction coefficient is as follows:

[0104] K1 = (η_actual / η_rated) * (1-D);

[0105] Where: D is the lifetime attenuation rate of the photovoltaic module.

[0106] In this example, it is assumed that the collected technical parameters are: the photovoltaic module is monocrystalline silicon, its rated conversion efficiency η_rated is 18%, and the degradation rate D over the expected service life is 0.5% per year. Assuming that the conversion efficiency of the module in the first year is the same as the rated conversion efficiency, that is, η_first_year = 18%, the conversion efficiency in the second year is as follows:

[0107] η_second_year=eta_first_year×(1-D)=18%×(1-0.5%)=17.91%;

[0108] The PV module type correction factor K1 is determined as follows:

[0109] K1=(n_second_year / n_rated)=17.91% / 18%≈0.995.

[0110] Furthermore, the method for confirming the tilt and azimuth correction coefficients of the photovoltaic array is as follows:

[0111] S15. Confirm the optimal inclination and azimuth of the photovoltaic array installation location, as well as the inclination and azimuth during actual installation;

[0112] S16. Taking the power generation of the photovoltaic array at the optimal inclination and azimuth as the reference condition, calculate the deviation between the power generation of the photovoltaic array at the inclination and azimuth during actual installation and the reference condition; the expression of the correction coefficient is as follows:

[0113] K2≈angle optimal / angle Actual ;

[0114] Where: angle optimal is the power generation under the best inclination and azimuth angle, angle Actual It is the power generation under the actual installation inclination and azimuth angles.

[0115] In this example, the baseline conditions are first determined: the optimal inclination and azimuth angles for the PV array installation location. Typically, the array faces due south, with an azimuth of 0° for maximum power generation. The inclination angle is the optimal local angle, typically close to the latitude, but may vary depending on the season and specific needs. The deviations of the actual inclination and azimuth angles from the baseline conditions are then calculated. The impact of these deviations on power generation can be assessed using PV simulation software (such as PVsyst or SAM) or empirical formulas. Based on this assessment, a correction factor, K2, is calculated to reflect the reduction in power generation due to deviations in inclination and azimuth.

[0116] Assuming that the function representing the relative power generation at a given inclination angle \theta and azimuth angle \phi compared to the baseline condition is (f(\theta, \phi)), K2 can be approximated as:

[0117] K2≈power generation at optimal inclination and azimuth angles / power generation at actual inclination and azimuth angles;

[0118] In this embodiment, it is assumed that the photovoltaic system is located at 40° north latitude, and the optimal tilt angle under baseline conditions is 40°, facing due south (azimuth 0°); however, due to site limitations, the actual installation tilt angle is 35° and the azimuth is 5° east.

[0119] Photovoltaic simulation software was then used to simulate the power generation under baseline conditions (40° inclination, 0° azimuth) and actual installation conditions (35° inclination, 5° east azimuth). The simulation results showed that the annual power generation under baseline conditions was 1000kWh / kWp, while the annual power generation under actual installation conditions was 950kWh / kWp.

[0120] The correction coefficient K2 is: K2 = 950 / 1000 = 0.95; this coefficient indicates that due to the deviation of the inclination angle and the azimuth angle, the actual power generation is reduced by 5% compared with the power generation under the reference conditions.

[0121] Furthermore, the method for confirming the inverter efficiency correction coefficient is as follows:

[0122] S17. Confirm the ideal efficiency data of the inverter and, based on the actual operating conditions of the photovoltaic modules, select the inverter efficiency value closest to the actual situation to obtain the actual efficiency data;

[0123] S18. Calculate the inverter efficiency correction factor based on the ideal efficiency data and the actual efficiency data. The expression of the inverter efficiency correction factor is as follows:

[0124] K3=K actual / K ideal ;

[0125] Among them: K actual is the actual efficiency data, K ideal It is the ideal efficiency data or the benchmark efficiency data (usually 100% or a value close to 1).

[0126] In this embodiment, inverter efficiency generally refers to the ratio of the inverter output power to the input power under specific operating conditions. Inverter efficiency is affected by various factors, including input voltage range, load conditions, ambient temperature, etc. The ideal efficiency data of the inverter can be obtained from the inverter manufacturer's technical manual or product specification sheet, such as the inverter's efficiency curve or efficiency data. This data may include efficiency values ​​under different load conditions or an average efficiency value.

[0127] Then, based on the actual operating conditions of the photovoltaic power generation system (such as sunshine intensity, load demand, etc.), select the inverter efficiency value that is closest to the actual situation; if the efficiency data is in the form of a curve, it may be necessary to calculate the efficiency value under specific conditions through interpolation or fitting formulas; if the inverter efficiency is high and close to the ideal value, then K3 is close to 1; if the inverter efficiency is low, then K3 is less than 1. However, in actual applications, the efficiency value provided by the inverter manufacturer is often used directly as K actual , and the ideal efficiency data K ideal Considered as 1 or setting a reference value close to 1 according to the actual situation), it can be simplified to: K3=K actual .

[0128] In this embodiment, it is assumed that the efficiency of the inverter used in a photovoltaic system is 95% under standard test conditions, that is, K actual =0.95, and the ideal efficiency data is set to 1 or 100%; in this case, the inverter efficiency correction coefficient K3 is directly equal to the actual efficiency value of the inverter, that is, K3 = 0.95.

[0129] Furthermore, the method for confirming the line loss correction coefficient is as follows:

[0130] S19. Determine the total resistance of the cables based on the resistivity, length, and cross-sectional area of ​​the cables in the photovoltaic module. The total resistance is expressed as follows:

[0131]

[0132] Where: R is the total resistance, ρ is the resistivity of the cable, L is the length, and A is the cross-sectional area;

[0133] S191. Determine the actual resistance loss power based on the total resistance and current passing through the cable. The expression for the resistance loss power is as follows:

[0134] P_loss=I 2 *R;

[0135] Where: P_loss is the actual resistance loss power, I 2 is the current flowing through the cable, and R is the total resistance of the cable;

[0136] S192. Compare the actual resistance loss power with the theoretical resistance loss power to confirm the line loss correction factor.

[0137] In this embodiment, line losses primarily include resistance loss and electromagnetic induction loss. Resistance loss is proportional to the square of the current and inversely proportional to the cable resistance. Electromagnetic induction loss (such as inductance and capacitance effects) is more significant in high-frequency currents but is generally not a major factor in photovoltaic systems.

[0138] When calculating losses, first determine the specifications and performance parameters of the PV modules, the efficiency of the inverter and other power electronics, the specifications, length, and material of the cables, and the layout and installation conditions of the PV system (such as temperature, humidity, and pollution level). The total cable resistance is then calculated using the cable's resistivity, length, and cross-sectional area. The resistive power loss is then calculated based on the current and cable resistance, and the resistive power loss is converted to energy loss. The system's operating time and efficiency are also considered, along with other factors such as the effect of ambient temperature on cable resistance (typically corrected using a temperature coefficient), the contact resistance and losses of cable connectors, and other non-ideal factors in the system, such as pollution and aging.

[0139] In this embodiment, the theoretical resistance loss power can be referred to the following table:

[0140] P_loss K4 [0,10W] 0.99 (10W, 15W] 0.97 ＞15W 0.95

[0141] In the line loss correction coefficient, theoretically, the resistance loss power is lossless, so the parameter value of K4 is 1. However, in actual situations, there is usually loss in the line, so the parameter value of K4 should be less than 1. In practice, it can be adjusted according to the actual situation. By testing the K4 values ​​corresponding to different resistance losses and the impact on the comprehensive efficiency coefficient, the appropriate values ​​of the actual resistance loss power and the theoretical resistance loss power can be finally determined.

[0142] In this example, it is assumed that the photovoltaic system uses a copper cable with a length of 100 meters, a cable cross-sectional area of ​​2.5 square millimeters, a system operating current of 10A, an ambient temperature of 25°C, and a resistivity of copper at 25°C of approximately 1.71*10-82·m; the calculated total cable resistance is R=1.7*10 -8 *100 / 2.5*10 -4 =00.0682.

[0143] The estimated resistance power loss is as follows:

[0144] P_loss=I 2 *R=10 2 *0.068 = 6.8W;

[0145] By looking up the benchmark resistor power loss table, 6.8W belongs to the [0,10W] range, so the value of K4 is 0.99.

[0146] In summary, taking a photovoltaic power station as an example, the process of determining its comprehensive efficiency coefficient K is as follows:

[0147] Assuming that the type of photovoltaic module used is a high-efficiency crystalline silicon module with a conversion efficiency of 18%, the correction coefficient K1 is 0.95; assuming that the inclination and azimuth angles of the array have been optimized, the correction coefficient K2 is 0.98 (indicating that the inclination and azimuth angles are set more reasonably and the efficiency loss is small); assuming that the efficiency of the inverter used is 97%, the correction coefficient K3 is 0.97; taking into account the line loss, the correction coefficient K4 is 0.99 (indicating that the line loss is small); assuming that the sum of the correction coefficients Kn of other influencing factors is 0.98 (indicating that the efficiency loss caused by other factors is small).

[0148] The comprehensive efficiency coefficient K is as follows:

[0149] K=0.95×0.98×0.97×0.99×0.98≈0.87.

[0150] S2. Based on the environmental and climatic factors of the photovoltaic power station area, a cloud motion model is constructed using machine learning methods. The cloud motion model is then used to simulate cloud motion to assess ground irradiation intensity and predict ultra-short-term photovoltaic power generation.

[0151] Further, specifically including:

[0152] S21. Collect satellite cloud image data in the photovoltaic power station area and use machine learning methods to simulate cloud movement and predict the degree of cloud movement blocking solar radiation, thereby evaluating the solar irradiance on the ground;

[0153] S22. Based on the solar irradiance on the ground and the measured irradiance at the current time, calculate the expected irradiance after the solar irradiance decays in the future; the expression of the expected irradiance is as follows:

[0154]

[0155] Where: E t+Δt is the expected irradiance after time Δt, E t is the measured irradiance at the current time, k is the cloud attenuation coefficient, C cloud is the proportion of area covered by clouds;

[0156] S23. Calculate the ultra-short-term photovoltaic power generation based on the expected irradiance and the conversion efficiency of the photovoltaic panels in the photovoltaic module. The calculation formula for the ultra-short-term photovoltaic power generation is as follows:

[0157] P=η×E t+Δt ×A;

[0158] Where: P is the ultra-short-term photovoltaic power generation power, η is the conversion efficiency of the photovoltaic panel, and A is the area of ​​the photovoltaic panel.

[0159] In this embodiment, it is first necessary to collect satellite cloud image data. This data is usually provided by meteorological satellites and can display information such as the distribution, thickness, and movement speed of clouds. Then, image processing technology is used to extract key features from the cloud image, such as the cloud coverage area, cloud type (such as cumulus, stratus, etc.), cloud movement direction and speed, etc. Then, based on the cloud characteristics and known meteorological data, a cloud motion model is established. For example, machine learning methods such as convolutional neural networks (CNN) and long short-term memory networks (LSTM) are used to simulate the movement of clouds over time.

[0160] When collecting satellite cloud image data of the photovoltaic power station area, satellite image or radar image sequences containing cloud movement are collected; these images contain timestamps to enable tracking of cloud movement; and meteorological data corresponding to the image sequences, such as wind speed, wind direction, and air pressure, are collected. Then, the image data needs to be preprocessed as necessary, such as denoising, normalization, and enhancement, while the meteorological data is appropriately processed, such as interpolation and standardization.

[0161] After preprocessing the data, CNN or other image processing techniques are used to extract cloud features such as shape, texture, density, etc. from the image to capture the static properties of the clouds. Then, by comparing the cloud positions in consecutive image frames, the motion features of the clouds, such as speed, acceleration, trajectory, etc., are calculated to capture the dynamic properties of the clouds. Finally, meteorological data is input into the model as an additional feature to consider the impact of meteorological conditions on cloud motion.

[0162] In application, CNN is used to extract the spatial features of clouds from images. The convolutional layers of CNN can learn local patterns in the image, while the pooling layers can capture the spatial hierarchy of these patterns. LSTM is used to capture the temporal dependencies of cloud motion. LSTM is a special recurrent neural network (RNN) that can process long-term dependencies in sequential data. The LSTM layer can receive the features extracted by CNN and meteorological features as input and predict the position and state of the clouds at the next time step. The outputs of CNN and LSTM are fused together to predict the final degree of cloud obstruction to solar radiation. This can be achieved through concatenation, addition, or other forms of feature fusion. Finally, based on the movement and characteristics of the clouds, the cloud motion model is used to predict the degree of cloud obstruction to solar radiation, and then the solar irradiance on the ground is predicted. The irradiance prediction results are combined with the performance parameters of the photovoltaic panels (such as conversion efficiency) to predict the photovoltaic power generation power.

[0163] S3. Based on the historical power generation data and weather information of the photovoltaic power station, a short-term photovoltaic power generation prediction model is constructed using a machine learning method. Then, the short-term photovoltaic power generation prediction model is used to predict the short-term photovoltaic power generation;

[0164] Further, specifically including:

[0165] S31. Collect historical photovoltaic power generation data and weather data of photovoltaic power stations and build a neural network model;

[0166] S32. Use historical photovoltaic power generation data, weather data, time characteristics, and recent power generation data as input features of the neural network model to make predictions and output the short-term photovoltaic power generation power in the future time period.

[0167] In this embodiment, historical photovoltaic power generation data and weather data of the photovoltaic power station are collected; the historical photovoltaic power generation data includes: historical photovoltaic power generation data of the photovoltaic power station, timestamps (such as data specific to hours or 15-minute intervals), solar radiation, temperature, humidity and other weather parameters.

[0168] The data is then cleaned, such as processing missing values ​​and outliers to ensure the integrity and accuracy of the data; then the data is standardized / normalized, such as converting data of different dimensions into the same dimension, usually by scaling the data to the range of 0 to 1, to avoid affecting the training effect of the model due to large differences in the numerical ranges of different features.

[0169] Next, select the features to build the neural network model, for example:

[0170] Time features: hour, date, day of the week, month, season, etc., to capture the periodic changes in photovoltaic power generation power; weather features: solar radiation, temperature, humidity, wind speed, wind direction, cloud coverage, etc., these features have a direct impact on photovoltaic power generation power; historical power data: power generation data over a period of time (such as a few hours or a day) is selected as input features, because the changes in photovoltaic power generation power usually have a certain degree of continuity.

[0171] Next, a neural network model is constructed. For short-term predictions, the model structure in this embodiment selects recursive neural networks such as LSTM or GRU because they can capture long-term dependencies in time series data. In the hidden layer, the ReLU (Rectified Linear Unit) activation function is selected to accelerate training and alleviate the gradient vanishing problem. In the output layer, since continuous values ​​(photovoltaic power generation) need to be predicted, no activation function is used or a linear activation function is used. For the loss function, the mean square error (MSE) or mean absolute error (MAE) loss function is selected to measure the accuracy of the model prediction. The optimizer uses methods such as Adam, RMSprop, or SGD (with momentum) to update the model weights during training.

[0172] The neural network model is then trained and evaluated. During training, the data set is first divided into a training set, a validation set, and a test set. The training set is used to train the model, the validation set is used to adjust model parameters and prevent overfitting, and the test set is used to evaluate the generalization ability of the model. The training set data is then used to train the neural network model, and the weights of the model are optimized through the backpropagation algorithm and the selected optimizer. The validation set is then used to evaluate the performance of the model, such as prediction accuracy, error rate, etc., and the model parameters or structure are adjusted according to the evaluation results. Finally, the test set is used to test the performance of the model to ensure that the model can maintain good predictive ability on unseen data.

[0173] Finally, the weather forecast data for the time period to be predicted is used as input data and input into the trained neural network model to obtain the predicted photovoltaic power generation value. The prediction results are then post-processed as needed, such as denormalization / normalization, smoothing, etc.

[0174] In this embodiment, it is assumed that the dataset contains medium- and long-term data such as photovoltaic power generation power, solar radiation, temperature, etc. every hour of every day in the past year, as well as weather forecast data for the next 24 hours. The prediction target is the photovoltaic power generation power every hour in the next 24 hours.

[0175] The photovoltaic power generation power, solar radiation, temperature and other data in the historical data are standardized. In addition to the three features mentioned above, time features (such as hour, date, and day of the week) and power generation data of the past few hours (such as the last 4 hours) are added as input features. A neural network model suitable for processing time series data (such as LSTM) is selected, and appropriate model parameters (such as the number of hidden layers and neurons) are set. The model is then trained using data from the past year as a training set, and the model performance is evaluated using a validation set. The weather forecast data and other relevant features for the next 24 hours are then input into the trained model to obtain the photovoltaic power generation power forecast value for each hour in the next 24 hours. The prediction results are then de-normalized and smoothed as much as possible to reduce the impact of noise.

[0176] Through this process, we can use the historical data and weather information of the power station itself or surrounding power stations to train a short-term photovoltaic power generation prediction model through artificial neural networks and make predictions.

[0177] In this embodiment, the ultra-short term in the ultra-short term photovoltaic power generation power refers to a time range of less than 4 hours, and the short term in the short term photovoltaic power generation power refers to a time range of less than 48 hours.

[0178] S4. Based on the comprehensive efficiency correction coefficient of the photovoltaic power station, the ultra-short-term photovoltaic power generation power, and the short-term photovoltaic power generation power, the photovoltaic power generation power of the photovoltaic power station in a certain future period is predicted. The expression for predicting the photovoltaic power generation power in a certain future period is as follows:

[0179] P_cyclel=K×(alpha×P_ultra_short+(1-alpha)×P_short);

[0180] Among them: P_cycle1 is the photovoltaic power generation power in a certain cycle in the future, K is the comprehensive efficiency correction coefficient of the photovoltaic power station, P_ultra_short is the ultra-short-term photovoltaic power generation power, P_short is the short-term photovoltaic power generation power, alpha is the weight coefficient, and the weight coefficient alpha is used to adjust the contribution of ultra-short-term and short-term prediction results in the final prediction. This coefficient can be determined based on factors such as the accuracy of historical data, the time range of the prediction, and the uncertainty of weather changes.

[0181] In the process of calculating the weight coefficient, an initial alpha value is set (such as 0.5, indicating that the weights of the two are equal), and then adjusted according to historical accuracy; if the historical accuracy of the ultra-short-term forecast is higher than that of the short-term forecast, the alpha value is increased.

[0182] If the forecast time range is taken into consideration, generally speaking, ultra-short-term forecasts may be more accurate because they are closer to the actual time point, so they tend to be given a higher weight. If the weather forecast shows that the weather will change significantly in the future, the weight of the short-term forecast may be reduced, that is, the complement of alpha 1-alpha is reduced; at the same time, a dynamic adjustment mechanism can be set to dynamically update the value of alpha based on new forecast results and real-time data.

[0183] The expression of the weight coefficient is as follows:

[0184]

[0185] Among them: A ultra _short is the accuracy index of ultra-short-term forecast, T factor is the time range factor, W factor is the uncertainty factor of weather change, A short is the accuracy index of short-term forecast; ultra_short With A short It refers to the measurement index used to reflect the accuracy of ultra-short-term and short-term forecasts. Its measurement function includes any one of the error rate, mean square error, root mean square error, and mean absolute error.

[0186] T factor =e -t ;

[0187] Where: λ is the decay rate, and t is the prediction time range.

[0188] In this embodiment, Altraa_short is an evaluation based on historical data, such as the inverse of accuracy, the inverse of error, or other indicators of accuracy. Altraa_short is a numerical value that reflects the accuracy and reliability of ultra-short-term forecasts (usually forecasts within 0-6 hours). This indicator can be a function of multiple metrics, including but not limited to error rate, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), etc.

[0189] Mean Squared Error (MSE) is the average of the squares of the differences between the predicted values ​​and the true values. It measures the average squared deviation between the predicted values ​​and the actual observed values.

[0190] Root Mean Squared Error (RMSE) is the square root of MSE and has the same units as MSE, making it easier to interpret.

[0191] Mean Absolute Error (MAE) MAE is the average of the absolute values ​​of the differences between the predicted values ​​and the true values. It measures the average absolute deviation between the predicted values ​​and the actual observed values.

[0192] A short It is also an evaluation based on historical data and is calculated in the same way as A ultra_short Similarly, it can also be a function of multiple metrics.

[0193] T factor is a factor used to adjust the effect of the forecast time range on the weight; generally speaking, as the forecast time range increases, the accuracy of the forecast may decrease, so T factor It may be a function that decreases as the prediction time increases.

[0194] For example, you can set a benchmark time range (such as 1 hour) and assign it a higher T factor value (such as 1.0), and then gradually reduce T as the prediction time range increases. factor value; such as using exponential decay, linear decay or other appropriate decay functions to simulate T factor A downward trend as the time frame increases, e.g., T factor =e -λt .

[0195] W factor It is used to reflect the impact of uncertainty in weather forecasts on forecast accuracy; the higher the uncertainty, the greater the factor The smaller the value of may be, the weather change uncertainty factor can be set in advance according to different weather conditions, fixed parameters, such as setting fixed parameters according to solar radiation intensity, temperature, wind speed, etc.

[0196] In the calculation formula of the weight coefficient, the numerator A ultra_short ·T factor W factor The "weighted accuracy" of ultra-short-term forecasts is calculated after considering the uncertainty of time horizon and weather changes; the denominator part A ultra_short ·T factor · Wfactor +A short (1-W factor ) Add the weighted accuracy of the ultra-short-term forecast to the weighted accuracy of the short-term forecast; it should be noted that the weighted accuracy of the short-term forecast is calculated by its accuracy index A short and (1-W factor ), which reflects that when the uncertainty of weather changes is high, the weight of short-term forecasts will be reduced accordingly.

[0197] Finally, the calculation result of the entire formula is: the value of alpha represents the contribution weight of the ultra-short-term prediction in the final prediction result; when the weighted accuracy of the ultra-short-term prediction is much higher than that of the short-term prediction, the value of alpha will be close to 1; conversely, when the weighted accuracy of the short-term prediction is higher, the value of alpha will be close to 0 (but in fact, since the weighted accuracy of the ultra-short-term prediction is always included in the denominator, alpha will not be equal to 0).

[0198] In this embodiment, the weight coefficient calculation formula is a heuristic method used to dynamically adjust the weights of ultra-short-term and short-term predictions in the final prediction based on multiple factors. However, in actual applications, this formula needs to be adjusted and optimized based on specific data and scenarios.

[0199] Assume K = 0.9 (indicates that the overall efficiency of the photovoltaic power station is 90%),

[0200] If P_ultra_short = 10000kW (indicating an ultra-short-term prediction value of 1000 kilowatts), P_short = 1050kW (indicating a short-term prediction value of 1050 kilowatts), and alpha = 0.6 (indicating a greater weight is given to the ultra-short-term prediction), the predicted photovoltaic power generation value in cycle 1 is as follows:

[0201]

[0202] In summary, the photovoltaic power generation project power generation prediction method of the present invention covers a variety of prediction technologies from ultra-short-term to medium- and long-term, combines direct prediction, indirect prediction and other prediction principles, and combines hybrid prediction methods such as machine learning and satellite remote sensing to improve the accuracy of photovoltaic power generation prediction.

[0203] Example 2:

[0204] See also Figure 2 A photovoltaic power station power generation prediction system is provided, the system being applied to the method described in Example 1, the system comprising:

[0205] The correction coefficient calculation module 1 is used to calculate the comprehensive efficiency correction coefficient of the photovoltaic power station; the calculation formula of the comprehensive efficiency correction coefficient is as follows:

[0206] K=K1×K2×K3×K4×...×Kn;

[0207] Among them: K is the comprehensive efficiency correction coefficient of the photovoltaic power station, K1 is the photovoltaic module type correction coefficient, K2 is the photovoltaic array tilt and azimuth correction coefficient, K3 is the inverter efficiency correction coefficient, K4 is the line loss correction coefficient, and Kn is the correction coefficient of other influencing factors;

[0208] Furthermore, the correction coefficient calculation module 1 calculates the coefficients according to the following methods:

[0209] The method for confirming the correction factor of the photovoltaic module type is as follows:

[0210] S11. Determine the material type of the photovoltaic module and collect relevant technical parameters of the photovoltaic module; the relevant technical parameters include conversion efficiency, maximum output power, open circuit voltage, and short circuit current;

[0211] S12. Based on relevant technical parameters of the photovoltaic module, determine the actual conversion efficiency of the photovoltaic module; the expression of the actual conversion efficiency is as follows:

[0212] η_actual=(Pmax / (Voc*Isc*FF))*100%;

[0213] Where: η_actual is the actual conversion efficiency of the photovoltaic module, Pmax is the maximum output power, Voc is the open circuit voltage, Isc is the short circuit current, and FF is the fill factor;

[0214] S13. Confirm the rated conversion efficiency of the photovoltaic module under standard test conditions; the expression for the rated conversion efficiency is as follows:

[0215] η_rated=(Pmax / (B*A))*100%;

[0216] Where: η_rated is the rated conversion efficiency of the photovoltaic module, B is the incident light power, and A is the area of ​​the photovoltaic module;

[0217] S14. Determine a photovoltaic module type correction factor based on the actual conversion efficiency and the rated conversion efficiency of the photovoltaic module. The expression of the photovoltaic module type correction factor is as follows:

[0218] K1=(η_actual / η_rated)×(1-D);

[0219] Where: D is the lifetime attenuation rate of the photovoltaic module.

[0220] The method for confirming the tilt and azimuth correction coefficients of the photovoltaic array is as follows:

[0221] S15. Confirm the optimal inclination and azimuth of the photovoltaic array installation location, as well as the inclination and azimuth during actual installation;

[0222] S16. Taking the power generation of the photovoltaic array at the optimal inclination and azimuth as the reference condition, calculate the deviation between the power generation of the photovoltaic array at the inclination and azimuth during actual installation and the reference condition; the expression of the correction coefficient is as follows:

[0223] K2≈angle optimal / angle Actual ;

[0224] Where: angle optimal is the power generation under the best inclination and azimuth angle, angle Actual It is the power generation under the actual installation inclination and azimuth angles.

[0225] The method for confirming the inverter efficiency correction factor is as follows:

[0226] S17. Confirm the ideal efficiency data of the inverter and, based on the actual operating conditions of the photovoltaic modules, select the inverter efficiency value closest to the actual situation to obtain the actual efficiency data;

[0227] S18. Calculate the inverter efficiency correction factor based on the ideal efficiency data and the actual efficiency data. The expression of the inverter efficiency correction factor is as follows:

[0228] K3=K actual / K ideal ;

[0229] Among them: K actual is the actual efficiency data, K ideal For ideal efficiency data.

[0230] The method for confirming the line loss correction factor is as follows:

[0231] S19. Determine the total resistance of the cables based on the resistivity, length, and cross-sectional area of ​​the cables in the photovoltaic module. The total resistance is expressed as follows:

[0232]

[0233] Where: R is the total resistance, ρ is the resistivity of the cable, L is the length, and A is the cross-sectional area;

[0234] S191. Determine the actual resistance loss power based on the total resistance and current passing through the cable. The expression for the resistance loss power is as follows:

[0235] P_loss=I 2 *R:

[0236] Where: P_loss is the actual resistance loss power, I 2 is the current flowing through the cable, and R is the total resistance of the cable;

[0237] S192. Compare the actual resistance loss power with the theoretical resistance loss power to confirm the line loss correction factor.

[0238] Ultra-short-term power prediction module 2 is used to construct a cloud motion model based on environmental and climatic factors in the photovoltaic power station area using machine learning methods. The cloud motion model then simulates cloud motion to assess ground irradiation intensity and predict ultra-short-term photovoltaic power generation.

[0239] Furthermore, the ultra-short-term power prediction module 2 predicts the ultra-short-term photovoltaic power generation power according to the following steps:

[0240] S21. Collect satellite cloud image data in the photovoltaic power station area and use machine learning methods to simulate cloud movement and predict the degree of cloud movement blocking solar radiation, thereby evaluating the solar irradiance on the ground;

[0241] S22. Based on the solar irradiance on the ground and the measured irradiance at the current time, calculate the expected irradiance after the solar irradiance decays in the future; the expression of the expected irradiance is as follows:

[0242]

[0243] Where: E t+Δt is the expected irradiance after time Δt, E t is the measured irradiance at the current time, k is the cloud attenuation coefficient, C cloud is the proportion of area covered by clouds;

[0244] S23. Calculate the ultra-short-term photovoltaic power generation based on the expected irradiance and the conversion efficiency of the photovoltaic panels in the photovoltaic module. The calculation formula for the ultra-short-term photovoltaic power generation is as follows:

[0245] P=η×E t+△ ×A;

[0246] Where: P is the ultra-short-term photovoltaic power generation power, η is the conversion efficiency of the photovoltaic panel, and A is the area of ​​the photovoltaic panel.

[0247] Short-term power prediction module 3 is used to build a short-term photovoltaic power generation prediction model based on the historical power generation data of the photovoltaic power station and weather information using a machine learning method, and then predict the short-term photovoltaic power generation power through the short-term photovoltaic power generation prediction model;

[0248] Furthermore, the short-term power prediction module 3 predicts the short-term photovoltaic power generation power according to the following steps:

[0249] S31. Collect historical photovoltaic power generation data and weather data of photovoltaic power stations and build a neural network model;

[0250] S32. Use historical photovoltaic power generation data, weather data, time characteristics, and recent power generation data as input features of the neural network model to make predictions and output the short-term photovoltaic power generation power in the future time period.

[0251] Photovoltaic power generation module 4 is used to predict the photovoltaic power generation power of the photovoltaic power station in a certain future cycle based on the comprehensive efficiency correction coefficient of the photovoltaic power station, the ultra-short-term photovoltaic power generation power, and the short-term period photovoltaic power generation power. The expression for predicting the photovoltaic power generation power in a certain future cycle is as follows:

[0252] Pcyclel=K×(alpha×P_ultra_short+(1-alpha)×P_short);

[0253] Among them: P_cclel is the photovoltaic power generation power in a certain period in the future, K is the comprehensive efficiency correction coefficient of the photovoltaic power station, P_ultra_short is the ultra-short-term photovoltaic power generation power, P_short is the short-term photovoltaic power generation power, and alpha is the weight coefficient.

[0254] Furthermore, the photovoltaic power generation module 4 calculates the weight coefficient according to the following expression:

[0255]

[0256] Among them: A ultra_short is the accuracy index of ultra-short-term forecast, T factor is the time range factor, W factor is the uncertainty factor of weather change, A shor t is the accuracy index of short-term forecast; ultra_short With A short It refers to the measurement index used to reflect the accuracy of ultra-short-term and short-term forecasts. Its measurement function includes any one of the error rate, mean square error, root mean square error, and mean absolute error.

[0257] T factor =e -λt ;

[0258] Where: λ is the decay rate, and t is the prediction time range.

[0259] Example 3:

[0260] See also Figure 3 , this embodiment further includes a photovoltaic power station power generation prediction device, the device including a processor 5 and a memory 6;

[0261] The memory 6 is used to store computer program code 61 and transmit the computer program code 61 to the processor 5;

[0262] The processor 5 is configured to execute the method for predicting the generated power of a photovoltaic power station according to the instructions in the computer program code 61 .

[0263] This embodiment further includes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed on a computer, the method for predicting the power generation of a photovoltaic power station described in Example 1 is implemented.

[0264] Generally speaking, computer instructions for implementing the method of the present invention may be carried by any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media except for signals that are temporarily propagating.

[0265] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0266] Computer program code for performing the operations of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, SMalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate 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 can 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 to an external computer (for example, using an Internet service provider to connect through the Internet).

[0267] The above-mentioned device and non-transitory computer-readable storage medium can be referred to the detailed description of a method for predicting power generation of a photovoltaic power station and its beneficial effects, which will not be repeated here.

[0268] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for predicting the power generation of a photovoltaic power station, characterized in that: include: S1. Calculate the comprehensive efficiency correction coefficient of the photovoltaic power station. The calculation formula of the comprehensive efficiency correction coefficient is as follows: K=K1×K2×K3×K4×...×Kn; Among them: K is the comprehensive efficiency correction coefficient of the photovoltaic power station, K1 is the photovoltaic module type correction coefficient, K2 is the photovoltaic array tilt and azimuth correction coefficient, K3 is the inverter efficiency correction coefficient, K4 is the line loss correction coefficient, and Kn is the correction coefficient of other influencing factors; S2. Based on the environmental and climatic factors of the photovoltaic power station area, a cloud motion model is constructed using machine learning methods. The cloud motion model is then used to simulate cloud motion to assess ground irradiation intensity and predict ultra-short-term photovoltaic power generation. S3. Based on the historical power generation data and weather information of the photovoltaic power station, a short-term photovoltaic power generation prediction model is constructed using a machine learning method. Then, the short-term photovoltaic power generation prediction model is used to predict the short-term photovoltaic power generation; S4. Based on the comprehensive efficiency correction coefficient of the photovoltaic power station, the ultra-short-term photovoltaic power generation power, and the short-term photovoltaic power generation power, the photovoltaic power generation power of the photovoltaic power station in a certain future period is predicted. The expression for predicting the photovoltaic power generation power in a certain future period is as follows: P_cycle1=K×(alpha×P_ultra_short+(1-alpha)×P_short); Among them: P_cycle1 is the photovoltaic power generation power in a certain cycle in the future, K is the comprehensive efficiency correction coefficient of the photovoltaic power station, P_ultra_short is the ultra-short-term photovoltaic power generation power, P_short is the short-term photovoltaic power generation power, and alpha is the weight coefficient.

2. The method for predicting power generation of a photovoltaic power station according to claim 1, wherein: The method for confirming the correction factor of the photovoltaic module type is as follows: S11. Determine the material type of the photovoltaic module and collect relevant technical parameters of the photovoltaic module; the relevant technical parameters include conversion efficiency, maximum output power, open circuit voltage, and short circuit current; S12. Based on relevant technical parameters of the photovoltaic module, determine the actual conversion efficiency of the photovoltaic module; the expression of the actual conversion efficiency is as follows: η_actual=(Pmax / (Voc*Isc*FF))*100%; Where: η_actual is the actual conversion efficiency of the photovoltaic module, Pmax is the maximum output power, Voc is the open circuit voltage, Isc is the short circuit current, and FF is the fill factor; S13. Confirm the rated conversion efficiency of the photovoltaic module under standard test conditions; the expression for the rated conversion efficiency is as follows: η_rated=(Pmax / (B*A))*100%; Where: η_rated is the rated conversion efficiency of the photovoltaic module, B is the incident light power, and A is the area of ​​the photovoltaic module; S14. Determine a photovoltaic module type correction factor based on the actual conversion efficiency and the rated conversion efficiency of the photovoltaic module. The expression of the photovoltaic module type correction factor is as follows: K1=(η_actual / η_rated)×(1-D); Where: D is the lifetime attenuation rate of the photovoltaic module.

3. The method for predicting power generation of a photovoltaic power station according to claim 1, wherein: The method for confirming the tilt and azimuth correction coefficients of the photovoltaic array is as follows: S15. Confirm the optimal inclination and azimuth of the photovoltaic array installation location, as well as the inclination and azimuth during actual installation; S16. Taking the power generation of the photovoltaic array at the optimal inclination and azimuth as the reference condition, calculate the deviation between the power generation of the photovoltaic array at the inclination and azimuth during actual installation and the reference condition; the expression of the correction coefficient is as follows: K2≈angle optimal / angle Actual ; Where: angle optimal is the power generation under the best inclination and azimuth angle, angle Actual It is the power generation under the actual installation inclination and azimuth angles.

4. The method for predicting power generation of a photovoltaic power station according to claim 1, wherein: The method for confirming the inverter efficiency correction factor is as follows: S17. Confirm the ideal efficiency data of the inverter and, based on the actual operating conditions of the photovoltaic modules, select the inverter efficiency value closest to the actual situation to obtain the actual efficiency data; S18. Calculate the inverter efficiency correction factor based on the ideal efficiency data and the actual efficiency data. The expression of the inverter efficiency correction factor is as follows: K3=K actual / K ideal ; Among them: K actual is the actual efficiency data, K ideal For ideal efficiency data.

5. The method for predicting power generation of a photovoltaic power station according to claim 1, wherein: The method for confirming the line loss correction factor is as follows: S19. Determine the total resistance of the cables based on the resistivity, length, and cross-sectional area of ​​the cables in the photovoltaic module. The total resistance is expressed as follows: Where: R is the total resistance, ρ is the resistivity of the cable, L is the length, and A is the cross-sectional area; S191. Determine the actual resistance loss power based on the total resistance and current passing through the cable. The expression for the resistance loss power is as follows: P_loss=I 2 *R; Where: P_loss is the actual resistance loss power, I 2 is the current flowing through the cable, and R is the total resistance of the cable; S192. Compare the actual resistance loss power with the theoretical resistance loss power to confirm the line loss correction factor.

6. The method for predicting power generation of a photovoltaic power station according to claim 1, wherein: The step S2 specifically includes: S21. Collect satellite cloud image data in the photovoltaic power station area and use machine learning methods to simulate cloud movement and predict the degree of cloud movement blocking solar radiation, thereby evaluating the solar irradiance on the ground; S22. Based on the solar irradiance on the ground and the measured irradiance at the current time, calculate the expected irradiance after the solar irradiance decays in the future; the expression of the expected irradiance is as follows: Where: E t+△t is the expected irradiance after time △t in the future, E t is the measured irradiance at the current time, k is the cloud attenuation coefficient, C cloud is the proportion of area covered by clouds; S23. Calculate the ultra-short-term photovoltaic power generation based on the expected irradiance and the conversion efficiency of the photovoltaic panels in the photovoltaic module. The calculation formula for the ultra-short-term photovoltaic power generation is as follows: P=η×E t+Δt ×A; Where: P is the ultra-short-term photovoltaic power generation power, η is the conversion efficiency of the photovoltaic panel, and A is the area of ​​the photovoltaic panel.

7. The method for predicting power generation of a photovoltaic power station according to claim 1, wherein: The step S3 specifically includes: S31. Collect historical photovoltaic power generation data and weather data of photovoltaic power stations and build a neural network model; S32. Use historical photovoltaic power generation data, weather data, time characteristics, and recent power generation data as input features of the neural network model to make predictions and output the short-term photovoltaic power generation power in the future time period.

8. The method for predicting power generation of a photovoltaic power station according to claim 1, wherein: In step S4, the expression of the weight coefficient is as follows: Among them: A ultra_short is the accuracy index of ultra-short-term forecast, T factor is the time range factor, W factor is the uncertainty factor of weather change, A short It is an indicator of the accuracy of short-term forecasts; T factor =e -λt ; Where: λ is the decay rate, and t is the prediction time range.

9. The method for predicting power generation of a photovoltaic power station according to claim 1, wherein: The ultra-short term in the ultra-short term photovoltaic power generation power refers to a time range within 4 hours, and the short term in the short term photovoltaic power generation power refers to a time range within 48 hours.

10. A photovoltaic power station power generation prediction system, characterized by: The system is applied to the method according to any one of claims 1 to 9, and the system comprises: The correction coefficient calculation module (1) is used to calculate the comprehensive efficiency correction coefficient of the photovoltaic power station; the calculation formula of the comprehensive efficiency correction coefficient is as follows: K=K1×K2×K3×K4×...×Kn; Among them: K is the comprehensive efficiency correction coefficient of the photovoltaic power station, K1 is the photovoltaic module type correction coefficient, K2 is the photovoltaic array tilt and azimuth correction coefficient, K3 is the inverter efficiency correction coefficient, K4 is the line loss correction coefficient, and Kn is the correction coefficient of other influencing factors; The ultra-short-term power prediction module (2) is used to construct a cloud motion model based on the environmental and climatic factors of the photovoltaic power station area using a machine learning method, and then simulate cloud motion through the cloud motion model to evaluate the ground radiation intensity and predict the ultra-short-term photovoltaic power generation power; A short-term power prediction module (3) is used to construct a short-term photovoltaic power generation prediction model based on the historical power generation data of the photovoltaic power station and weather information using a machine learning method, and then predict the short-term photovoltaic power generation power through the short-term photovoltaic power generation prediction model; The photovoltaic power generation module (4) is used to predict the photovoltaic power generation power of the photovoltaic power station within a certain future cycle based on the comprehensive efficiency correction coefficient of the photovoltaic power station, the ultra-short-term photovoltaic power generation power, and the short-term period photovoltaic power generation power; the expression for predicting the photovoltaic power generation power within a certain future cycle is as follows: P_cycle1=K×(alpha×P_ultra_short+(1-alpha)×P_short); Among them: P_cycle1 is the photovoltaic power generation power in a certain cycle in the future, K is the comprehensive efficiency correction coefficient of the photovoltaic power station, P_ultra_short is the ultra-short-term photovoltaic power generation power, P_short is the short-term photovoltaic power generation power, and alpha is the weight coefficient.

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