A photovoltaic power station power prediction method and device

Abstract

This invention provides a method and apparatus for predicting the power output of a photovoltaic (PV) power plant. The method includes: constructing a target PV power plant power prediction model based on factors influencing the power output of the PV power plant and the relationships between these factors. These factors include the annual degradation efficiency of PV modules, cell temperature of PV modules, transmission and transformation efficiency of the PV power plant, weather-related efficiency, weather-related constants, the power temperature coefficient of PV modules, the total area of ​​PV panels in the PV power plant, cell temperature under standard testing conditions for PV modules, and the number of years the PV modules have been in operation. The method also involves acquiring real-time data from the PV power plant and predicting the power output of the PV power plant based on this real-time data and the target PV power plant power prediction model. This invention accurately predicts the power output of PV power plants by constructing a target PV power plant power prediction model, and uses the prediction results to formulate power grid dispatch plans, reduce power grid fluctuations, and achieve a balance between power supply and demand.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and in particular to a method and apparatus for predicting the power output of a photovoltaic power plant. Background Technology

[0002] Solar energy is an inexhaustible renewable energy source for humankind, possessing advantages such as high cleanliness, absolute safety, relative widespread availability, abundant resources, and potential economic viability, thus holding an important position in long-term energy strategies. With the introduction of the "dual carbon target," a large number of photovoltaic power plants have been put into operation, making the grid's achievement of a balance between electricity supply and demand a significant challenge.

[0003] Because photovoltaic power generation systems are affected by factors such as ambient temperature, wind speed, photovoltaic panel degradation, photovoltaic power plant transmission and transformation efficiency, and weather, photovoltaic power generation has strong randomness, volatility, and uncertainty. Existing technologies do not take into account the influence of these factors, making it impossible to accurately predict the power output of photovoltaic power plants. This increases the difficulty and complexity of grid dispatching, causes large grid fluctuations, and poses a challenge to the power supply and demand balance of the grid.

[0004] Therefore, there is an urgent need to propose a method and device for predicting the power of photovoltaic power plants, in order to improve the accuracy of predicting the power of photovoltaic power plants. Summary of the Invention

[0005] In view of this, it is necessary to provide a method and apparatus for predicting the power of photovoltaic power plants in order to solve the problem of inaccurate power prediction of photovoltaic power plants.

[0006] On one hand, the present invention provides a method for predicting the power output of a photovoltaic power plant, comprising:

[0007] A target photovoltaic power plant power prediction model is constructed based on the related factors affecting the power of photovoltaic power plants and the correlation between these factors. The related factors in the target photovoltaic power plant power prediction model include the annual degradation efficiency of photovoltaic modules, the cell temperature of photovoltaic modules, the transmission and transformation efficiency of photovoltaic power plants, the efficiency affected by weather, the weather-related constant, the power temperature coefficient of photovoltaic modules, the total area of ​​photovoltaic panels in photovoltaic power plants, the cell temperature in the standard test conditions of photovoltaic modules, and the number of years of operation of photovoltaic modules.

[0008] Acquire real-time data of the photovoltaic power station, and predict the power of the photovoltaic power station to be predicted based on the real-time data and the target photovoltaic power station power prediction model.

[0009] In some possible implementations, the correlation factors include known and unknown correlation factors. The known correlation factors include the total area of ​​the photovoltaic panels in the photovoltaic power station, the cell temperature under standard testing conditions for the photovoltaic modules, the number of years the photovoltaic modules have been in operation, and the annual degradation efficiency of the photovoltaic modules. The unknown correlation factors include the efficiency of the photovoltaic power station's transmission and transformation station, the power temperature coefficient of the photovoltaic modules, the weather-related efficiency, and the weather-related constant. The construction of a target photovoltaic power station power prediction model based on the correlation factors affecting the photovoltaic power station's power and the relationships between these factors includes:

[0010] Obtain the total irradiance of the photovoltaic panels in the photovoltaic power station, the wind speed at the preset height, and the ambient temperature;

[0011] An initial photovoltaic power prediction model is constructed based on the total irradiance of the photovoltaic panels of the photovoltaic power station, the wind speed at the preset height, the ambient temperature, the known correlation factors, and the unknown correlation factors.

[0012] Historical data is acquired, and the initial photovoltaic power plant power prediction model is solved based on the historical data to determine the target photovoltaic power plant power prediction model.

[0013] In some possible implementations, the target photovoltaic power plant power prediction model is as follows:

[0014]

[0015] The weather impact efficiency η w for:

[0016]

[0017] Where P represents the power output of the photovoltaic power station, A represents the total area of ​​the photovoltaic panels in the photovoltaic power station, and I represents the total irradiance of the photovoltaic power station. T is the power temperature coefficient of the photovoltaic module, and T′ is the temperature of the photovoltaic module cell. z η represents the cell temperature under standard test conditions for photovoltaic modules, n represents the number of years the photovoltaic module has been in operation, and η represents the cell temperature. n For the annual degradation efficiency of photovoltaic modules, η s The power transmission and transformation efficiency of the photovoltaic power station is given by k1, k2, k3, and k4, which are weather-related constants, m is the number of weather types, and η is the value of η. wm Let m be the weather impact efficiency for the m-th weather type.

[0018] In some possible implementations, the historical data includes: wind speed at a historical preset altitude, historical ambient temperature, historical photovoltaic power plant power output under various weather conditions, and historical total irradiance of the photovoltaic power plant under various weather conditions.

[0019] In some possible implementations, solving the initial photovoltaic power plant power prediction model based on the historical data to determine the target photovoltaic power plant power prediction model includes:

[0020] The historical photovoltaic module cell temperature is determined based on the wind speed at the historical preset altitude, the historical ambient temperature, and the photovoltaic module cell temperature calculation formula.

[0021] The power transmission and transformation efficiency of the photovoltaic power station is determined based on the historical power of the photovoltaic power station under various weather conditions, the DC-side power of the photovoltaic power station under various weather conditions, and the first fitting formula.

[0022] Based on the historical photovoltaic module temperature, the historical photovoltaic power plant power under various weather conditions, the historical total irradiance of the photovoltaic power plant under various weather conditions, the photovoltaic power plant transmission and transformation efficiency, and the second fitting formula, the weather impact efficiency and the weather-related constant are determined.

[0023] The photovoltaic module power temperature coefficient is determined based on the historical photovoltaic power plant power under various weather conditions, the historical total irradiance of the photovoltaic power plant under various weather conditions, the photovoltaic power plant transmission and transformation efficiency, the weather-related efficiency, the weather-related constant, the historical photovoltaic module temperature, and the third fitting formula.

[0024] In some possible implementations, the formula for calculating the temperature of the photovoltaic module cells is:

[0025] T′=T b +I·k5

[0026]

[0027] Among them, T b V represents the historical backsheet temperature of photovoltaic modules. w The wind speed at the historical preset height, T e K represents the historical ambient temperature, and k5, k6, and k7 are temperature-related constants.

[0028] In some possible implementations, the first fitting formula is:

[0029]

[0030] Where P' represents the historical photovoltaic power output under various weather conditions, P z The DC-side power of the photovoltaic power station is based on various weather conditions.

[0031] In some possible implementations, the second fitting formula is:

[0032]

[0033] Among them, Pmi Let I be the historical photovoltaic power output for the m-th weather condition. mi Let be the total historical irradiance of the photovoltaic power station under the m-th weather condition.

[0034] In some possible implementations, the third fitting formula is:

[0035]

[0036] On the other hand, the present invention also provides a photovoltaic power plant power prediction device, comprising:

[0037] A target model unit is constructed, which is based on the related factors affecting the power of the photovoltaic power station and the correlation between the related factors to construct a target photovoltaic power station power prediction model. The related factors in the target photovoltaic power station power prediction model include the annual degradation efficiency of photovoltaic modules, the cell temperature of photovoltaic modules, the transmission and transformation efficiency of photovoltaic power station, the efficiency affected by weather, the weather-related constant, the power temperature coefficient of photovoltaic modules, the total area of ​​photovoltaic panels in photovoltaic power station, the cell temperature in the standard test conditions of photovoltaic modules, and the number of years of operation of photovoltaic modules.

[0038] The prediction unit acquires real-time data from the photovoltaic power station and uses this data to predict the power of the photovoltaic power station to be predicted based on the target photovoltaic power station power prediction model.

[0039] The beneficial effects of the above embodiments are as follows: The photovoltaic power plant power prediction method provided by the present invention constructs a target photovoltaic power plant power prediction model by considering the related factors affecting the power of the photovoltaic power plant and the correlation between the related factors; it acquires real-time data of the photovoltaic power plant and predicts the power of the photovoltaic power plant based on the target photovoltaic power plant power model. This model comprehensively considers the influence of photovoltaic module cell temperature, photovoltaic power plant transmission and transformation efficiency, weather-related efficiency, weather-related constants, photovoltaic module power temperature coefficient, and photovoltaic module annual decay efficiency on power, thereby improving the accuracy of the prediction model in predicting the power of the photovoltaic power plant. As a result, it can formulate power grid dispatch plans, reduce power grid fluctuations, and achieve power supply and demand balance based on the prediction results. Attached Figure Description

[0040] Figure 1 This is a schematic flowchart of an embodiment of the photovoltaic power plant power prediction method provided by the present invention;

[0041] Figure 2 For the present invention Figure 1 A schematic diagram of an embodiment of step S101;

[0042] Figure 3 For the present invention Figure 1 A schematic flowchart of an embodiment of step S203;

[0043] Figure 4 This is a schematic diagram of an embodiment of the photovoltaic power plant power prediction device provided by the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0045] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, "multiple" means at least two, such as two types, three types, etc., unless otherwise explicitly specified.

[0046] This invention provides a method and apparatus for predicting the power output of a photovoltaic power plant, which will be described below.

[0047] Figure 1 This is a schematic flowchart of an embodiment of the photovoltaic power plant power prediction method provided by the present invention, as shown below. Figure 1 As shown, the photovoltaic power plant power prediction methods include:

[0048] S101. Based on the factors affecting the power of a photovoltaic power station and the relationships between these factors, a target photovoltaic power station power prediction model is constructed. The target photovoltaic power station power prediction model includes the annual degradation efficiency of photovoltaic modules, the cell temperature of photovoltaic modules, the transmission and transformation efficiency of photovoltaic power stations, the efficiency affected by weather, the weather-related constant, the power temperature coefficient of photovoltaic modules, the total area of ​​photovoltaic panels in photovoltaic power stations, the cell temperature in the standard test conditions of photovoltaic modules, and the number of years of operation of photovoltaic modules.

[0049] S102. Obtain real-time data of the photovoltaic power station, and predict the power of the photovoltaic power station to be predicted based on the real-time data of the photovoltaic power station and the target photovoltaic power station power prediction model.

[0050] It should be noted that, considering the influence of factors such as ambient temperature, wind speed, photovoltaic panel degradation, photovoltaic power plant transmission and transformation efficiency, and weather, photovoltaic power generation has strong randomness, volatility, and uncertainty. Therefore, the relevant factors are introduced into the target photovoltaic power plant power prediction model to improve the accuracy of power prediction.

[0051] It should also be noted that the real-time data of the photovoltaic power station includes real-time ambient temperature, real-time wind speed at the preset height, and real-time total irradiance of the photovoltaic power station;

[0052] The beneficial effects of the above embodiments are as follows: The photovoltaic power plant power prediction method provided by the present invention predicts the power of a photovoltaic power plant by constructing a target photovoltaic power plant power prediction model through the correlation factors affecting the power of the photovoltaic power plant and the correlation between the correlation factors; it obtains real-time data of the photovoltaic power plant and predicts the power of the photovoltaic power plant based on the target photovoltaic power plant power model. This model comprehensively considers the influence of photovoltaic module cell temperature, photovoltaic power plant transmission and transformation efficiency, weather-related efficiency, weather-related constants, photovoltaic module power temperature coefficient, and photovoltaic module annual decay efficiency on power, thereby improving the accuracy of the prediction model in predicting the power of the photovoltaic power plant. As a result, it can formulate power grid dispatching plans, reduce power grid fluctuations, and achieve power supply and demand balance based on the prediction results.

[0053] In some specific embodiments of the present invention, the related factors include known related factors and unknown related factors. The known related factors include the total area of ​​photovoltaic panels in the photovoltaic power station, the cell temperature in the standard test conditions of the photovoltaic module, the number of years the photovoltaic module has been in operation, and the annual degradation efficiency of the photovoltaic module. The unknown related factors include the efficiency of the photovoltaic power station transmission and transformation station, the power temperature coefficient of the photovoltaic module, the efficiency affected by weather, and the weather-related constant.

[0054] In some embodiments of the present invention, such as Figure 2 As shown, step S101 includes:

[0055] S201. Obtain the total area of ​​photovoltaic panels in the photovoltaic power station, the wind speed at the preset height, and the ambient temperature;

[0056] S202. Construct an initial photovoltaic power prediction model based on the total area of ​​photovoltaic panels in the photovoltaic power station, wind speed at the preset height, ambient temperature, known and unknown related factors;

[0057] S203. Obtain historical data, solve the initial photovoltaic power plant power prediction model based on the historical data, and determine the target photovoltaic power plant power prediction model.

[0058] It should be noted that: known correlation factors refer to the correlation factors that need to be obtained in the initial photovoltaic power plant power prediction model and whose influence is known; unknown correlation factors refer to the correlation factors that need to be obtained in the initial photovoltaic power plant power prediction model but whose influence is unknown.

[0059] In some embodiments of the present invention, the target photovoltaic power plant power prediction model is as follows:

[0060]

[0061] Weather affects efficiency η w for:

[0062]

[0063] Where P represents the power output of the photovoltaic power station, A represents the total area of ​​the photovoltaic panels in the photovoltaic power station, and I represents the total irradiance of the photovoltaic power station. T is the power temperature coefficient of the photovoltaic module, and T' is the temperature of the photovoltaic module cells. z η represents the cell temperature under standard test conditions for photovoltaic modules, n represents the number of years the photovoltaic module has been in operation, and η represents the cell temperature. n For the annual degradation efficiency of photovoltaic modules, η s The power transmission and transformation efficiency of the photovoltaic power station is given by k1, k2, k3, and k4, which are weather-related constants, m is the number of weather types, and η is the value of η. wm Let m be the weather impact efficiency for the m-th weather type.

[0064] This invention improves the accuracy of the target photovoltaic power plant power prediction model by taking into account the influence of photovoltaic module power temperature coefficient, photovoltaic module cell temperature, photovoltaic module annual degradation efficiency, photovoltaic power plant transmission and transformation efficiency, and weather impact efficiency on photovoltaic power plant power.

[0065] In a specific embodiment of the present invention, the historical data includes: wind speed at a historical preset altitude, historical ambient temperature, historical photovoltaic power station power under various weather conditions, and historical total irradiance of the photovoltaic power station under various weather conditions.

[0066] In some embodiments of the present invention, such as Figure 3 As shown, step S203 includes:

[0067] S301. Determine the historical photovoltaic module cell temperature based on the historical wind speed at the preset altitude, historical ambient temperature, and the photovoltaic module cell temperature calculation formula.

[0068] S302. Determine the power transmission and transformation efficiency of the photovoltaic power station based on the historical power of the photovoltaic power station under various weather conditions, the DC-side power of the photovoltaic power station under various weather conditions, and the first fitting formula.

[0069] S303. Based on historical photovoltaic module cell temperature, historical photovoltaic power plant power under various weather conditions, historical photovoltaic power plant total irradiance under various weather conditions, photovoltaic power plant transmission and transformation efficiency, and the second fitting formula, determine the weather-related efficiency and weather-related constants.

[0070] S304. Based on the historical power of photovoltaic power plants under various weather conditions, the total irradiance of photovoltaic power plants under various weather conditions, the transmission and transformation efficiency of photovoltaic power plants, the efficiency affected by weather, the weather-related constant, the historical temperature of photovoltaic module cells, and the third fitting formula, determine the power temperature coefficient of photovoltaic modules.

[0071] This invention improves the accuracy and reliability of the calculated photovoltaic power plant transmission and transformation efficiency, weather-related efficiency, weather-related constants, and photovoltaic module power temperature coefficient by acquiring a large amount of historical data and fitting the historical data, thereby reducing the impact of errors in the historical data.

[0072] In some embodiments of the present invention, the temperature of the photovoltaic module cells is calculated as follows:

[0073] T′=T b +I·k5

[0074]

[0075] Among them, T b V represents the historical backsheet temperature of photovoltaic modules. w The wind speed at the historical preset height, T e K represents the historical ambient temperature, and k5, k6, and k7 are temperature-related constants.

[0076] In a specific embodiment of the present invention, the preset height can be ten meters;

[0077] In some embodiments of the present invention, the first fitting formula is:

[0078]

[0079] Where P′ represents the historical photovoltaic power output under various weather conditions, P z The DC-side power of the photovoltaic power station is based on various weather conditions.

[0080] In some embodiments of the present invention, the second fitting formula is:

[0081]

[0082] Among them, P mi Let I be the historical photovoltaic power output for the m-th weather condition. mi Let be the total historical irradiance of the photovoltaic power station under the m-th weather condition.

[0083] It should be noted that the various weather conditions in this embodiment refer to four, six, eight, or other specific weather situations.

[0084] In a specific embodiment of the present invention, the various weather conditions can specifically refer to four types: sunny, cloudy, rainy, and snowy. When the photovoltaic module cell temperature T′ is 25°C, the photovoltaic power station power and the actual irradiance of the photovoltaic power station are linearly related. Historical data on the photovoltaic power station power and the actual irradiance of the photovoltaic power station under the four weather conditions when the photovoltaic module cell temperature is 25°C are selected, and fitted using a second fitting formula to obtain the efficiency of the four weather conditions and the weather-related constants.

[0085] The second fitting formula is as follows:

[0086]

[0087]

[0088]

[0089]

[0090] Among them, P 1i P 2i P 3i and P 4i Historical photovoltaic power output for sunny, cloudy, rainy, and snowy conditions, respectively. 1i I 2i I 3i and I 4i The actual historical irradiance of photovoltaic power stations under sunny, cloudy, rainy, and snowy conditions are represented by η. w1 η w2 η w3 and η w4 The effects of sunny, cloudy, rainy, and snowy weather on efficiency are respectively.

[0091] In some embodiments of the present invention, the third fitting formula is:

[0092]

[0093] It should be noted that the third fitting formula is obtained by transforming the initial photovoltaic power plant power prediction model. At this time, the photovoltaic power plant transmission and transformation efficiency, weather impact efficiency, and weather-related constants have been solved from the first and second fitting formulas, and the solved parameters can be directly substituted into the third fitting formula.

[0094] It should also be noted that after obtaining the photovoltaic module power temperature coefficient through the third fitting formula, the following parameters are already solved in the target photovoltaic power plant power prediction model: the photovoltaic power plant transmission and transformation efficiency, photovoltaic module power temperature coefficient, weather-related efficiency, and weather-related constants. The total area of ​​the photovoltaic panels, the number of years the photovoltaic modules have been in operation, the cell temperature under standard testing conditions, the annual degradation efficiency of the photovoltaic modules, and the temperature-related constants are known. Therefore, by obtaining the wind speed at a preset altitude, the ambient temperature, and the actual irradiance of the photovoltaic power plant, the power can be predicted using the target photovoltaic power plant power prediction model, thereby improving the accuracy of the predicted power while reducing the complexity of the prediction.

[0095] To better implement the photovoltaic power plant power prediction method in the embodiments of the present invention, based on the photovoltaic power plant power prediction method, the embodiments of the present invention also provide a photovoltaic power plant power prediction device, such as... Figure 4 As shown, the photovoltaic power plant power prediction device 400 includes:

[0096] The target model unit 401 is used to construct a target photovoltaic power station power prediction model based on the related factors affecting the power of the photovoltaic power station and the correlation between the related factors. The related factors in the target photovoltaic power station power prediction model include the annual degradation efficiency of photovoltaic modules, the cell temperature of photovoltaic modules, the transmission and transformation efficiency of photovoltaic power station, the efficiency affected by weather, the weather-related constant and the power temperature coefficient of photovoltaic modules, the total area of ​​photovoltaic panels in photovoltaic power station, the cell temperature in the standard test conditions of photovoltaic modules and the number of years of operation of photovoltaic modules.

[0097] The prediction unit 402 is used to acquire real-time data of the photovoltaic power station and predict the power of the photovoltaic power station to be predicted based on the real-time data of the photovoltaic power station and the target photovoltaic power station power prediction model.

[0098] The photovoltaic power plant power prediction device 400 provided in the above embodiments can realize the technical solutions described in the above photovoltaic power plant power prediction method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above photovoltaic power plant power prediction method embodiments, which will not be repeated here.

[0099] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for power prediction of a photovoltaic power plant, characterized in that, include: A target photovoltaic power station power prediction model is constructed based on the factors affecting the power output of a photovoltaic power station and the relationships between these factors. This includes: obtaining the total irradiance of the photovoltaic power station, wind speed at a preset altitude, and ambient temperature; constructing an initial photovoltaic power station power prediction model based on the total irradiance, wind speed at the preset altitude, ambient temperature, known factors, and unknown factors; acquiring historical data, and solving the initial photovoltaic power station power prediction model based on the historical data to determine the target photovoltaic power station power prediction model. The factors include known and unknown factors. Known factors include the total area of ​​the photovoltaic panels in the photovoltaic power station, cell temperature under standard testing conditions for photovoltaic modules, the number of years the photovoltaic modules have been in operation, and the annual degradation efficiency of the photovoltaic modules. Unknown factors include the efficiency of the photovoltaic power station's transmission and transformation station, the power temperature coefficient of the photovoltaic modules, weather-related efficiency, and weather-related constants. Acquire real-time data of photovoltaic power plants, and predict the power of the photovoltaic power plant to be predicted based on the real-time data of the photovoltaic power plants and the power prediction model of the target photovoltaic power plants; The power prediction model for the target photovoltaic power plant is as follows: The efficiency of the weather impact is: in, P For photovoltaic power station power, A The total area of ​​the photovoltaic panels in the photovoltaic power station. I The total irradiance of the photovoltaic power station. The power temperature coefficient of photovoltaic modules. T' For the temperature of photovoltaic module cells, Tz The cell temperature under standard test conditions for photovoltaic modules. n The number of years the photovoltaic modules have been in operation. ηn For the annual degradation efficiency of photovoltaic modules, η s represents the power transmission and transformation efficiency of the photovoltaic power station. k 1. k 2. k 3. k 4 represents weather-related constants. m For the number of weather types, ηwm For the first m The weather affects efficiency.

2. The photovoltaic power plant power prediction method according to claim 1, characterized in that, The historical data includes: wind speed at historical preset altitudes, historical ambient temperature, historical photovoltaic power generation under various weather conditions, and historical total irradiance of photovoltaic power stations under various weather conditions.

3. The photovoltaic power plant power prediction method according to claim 2, characterized in that, The step of solving the initial photovoltaic power plant power prediction model based on the historical data to determine the target photovoltaic power plant power prediction model includes: The historical photovoltaic module cell temperature is determined based on the wind speed at the historical preset altitude, the historical ambient temperature, and the photovoltaic module cell temperature calculation formula. The power transmission and transformation efficiency of the photovoltaic power station is determined based on the historical power of the photovoltaic power station under various weather conditions, the DC-side power of the photovoltaic power station under various weather conditions, and the first fitting formula. Based on the historical photovoltaic module cell temperature, the historical photovoltaic power plant power under various weather conditions, the historical total irradiance of the photovoltaic power plant under various weather conditions, the power transmission and transformation efficiency of the photovoltaic power plant, and the second fitting formula, the weather-related efficiency and the weather-related constant are determined. The photovoltaic module power temperature coefficient is determined based on the historical power of the photovoltaic power station under various weather conditions, the total irradiance of the photovoltaic power station under various weather conditions, the power transmission and transformation efficiency of the photovoltaic power station, the weather-related efficiency, the weather-related constant, the historical photovoltaic module cell temperature, and a third fitting formula.

4. The photovoltaic power plant power prediction method according to claim 3, characterized in that, The formula for calculating the temperature of the photovoltaic module cells is as follows: in, Tb Historical photovoltaic module backsheet temperature Vw Wind speeds that are pre-set to historical highs, Te Historical environmental temperature, k5 , k6 and k7 This is a temperature-dependent constant.

5. The photovoltaic power plant power prediction method according to claim 3, characterized in that, The first fitting formula is: in, P' Historical photovoltaic power generation under various weather conditions Pz The DC-side power of the photovoltaic power station is based on various weather conditions.

6. The photovoltaic power plant power prediction method according to claim 3, characterized in that, The second fitting formula is: in, P mi For the first m Historical photovoltaic power generation capacity for various weather conditions I mi For the first m The total irradiance of photovoltaic power plants in the history of this weather.

7. The photovoltaic power plant power prediction method according to claim 3, characterized in that, The third fitting formula is: 。 8. A photovoltaic power plant power prediction device, characterized in that, The apparatus for performing the photovoltaic power plant power prediction method as described in any one of claims 1-7 includes: A target model unit is constructed to build a target photovoltaic power station power prediction model based on the related factors affecting the power of the photovoltaic power station and the correlation between the related factors. The related factors in the target photovoltaic power station power prediction model include the annual degradation efficiency of photovoltaic modules, the cell temperature of photovoltaic modules, the transmission and transformation efficiency of photovoltaic power station, the efficiency affected by weather, the weather-related constant and the power temperature coefficient of photovoltaic modules, the total area of ​​photovoltaic panels in photovoltaic power station, the cell temperature in the standard test conditions of photovoltaic modules and the number of years of operation of photovoltaic modules. The prediction unit is used to acquire real-time data of the photovoltaic power station and predict the power of the photovoltaic power station to be predicted based on the real-time data of the photovoltaic power station and the power prediction model of the target photovoltaic power station.

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